This document discusses using bilateral filtering to extract channel structures from 3D seismic data. It begins with an introduction to bilateral filtering and its advantages over traditional Gaussian filtering. The document then provides mathematical definitions of Gaussian and bilateral filtering. It applies bilateral filtering to examples of synthetic 1D data and a slice of 3D seismic volume data to demonstrate how it can extract channel edges while preserving features. The document concludes by discussing parameter selection and computational costs of bilateral filtering.
Image Denoising Using Earth Mover's Distance and Local HistogramsCSCJournals
In this paper an adaptive range and domain filtering is presented. In the proposed method local histograms are computed to tune the range and domain extensions of bilateral filter. Noise histogram is estimated to measure the noise level at each pixel in the noisy image. The extensions of range and domain filters are determined based on pixel noise level. Experimental results show that the proposed method effectively removes the noise while preserves the details. The proposed method performs better than bilateral filter and restored test images have higher PSNR than those obtained by applying popular Bayesshrink wavelet denoising method.
Accelerated Joint Image Despeckling Algorithm in the Wavelet and Spatial DomainsCSCJournals
Noise is one of the most widespread problems present in nearly all imaging applications. In spite of the sophistication of the recently proposed methods, most denoising algorithms have not yet attained a desirable level of applicability. This paper proposes a two-stage algorithm for speckle noise reduction jointly in the wavelet and spatial domains. At the first stage, the optimal parameter value of the spatial speckle reduction filter is estimated, based on edge pixel statistics and noise variance. Then the optimized filter is used at the second stage to additionally smooth the approximation image of the wavelet sub-band. A complexity reduction algorithm for wavelet decomposition is also proposed. The obtained results are highly encouraging in terms of image quality which paves the way towards the reinforcement of the proposed algorithm for the performance enhancement of the Block Matching and 3D Filtering algorithm tackling multiplicative speckle noise.
OBIA on Coastal Landform Based on Structure Tensor csandit
This paper presents the OBIA method based on structure tensor to identify complex coastal
landforms. That is, develop Hessian matrix by Gabor filtering and calculate multiscale structure
tensor. Extract edge information of image from the trace of structure tensor and conduct
watershed segment of the image. Then, develop texons and create texton histogram. Finally,
obtain the final results by means of maximum likelihood classification with KL divergence as
the similarity measurement. The study findings show that structure tensor could obtain
multiscale and all-direction information with small data redundancy. Moreover, the method
described in the current paper has high classification accuracy
Analysis of Adaptive and Advanced Speckle Filters on SAR DataIOSRjournaljce
Synthetic Aperture RADAR(SAR) images get inherently affected by speckle noise which is multiplicative in nature. This noise affects the image spatial statistics and properties. Over the past several years, many SAR denoising algorithms have been developed to reduce speckle noise. Some of the standard speckle filters are Gamma MAP, Lee, Frost and Kuan filters. Further, these have also been modified to obtain better results after filtering, than their original counterparts. Apart from the standard speckle filters, advanced SAR filters like Block Matching 3 Dimensional (BM3D) are also present. In this paper several standard as well as advanced speckle filters have been analyzed and compared. For comparison, Quality Assessment has been performed where the filtered images are compared to each other using parameters like Radiometric Resolution and others. These parameters help to distinguish the performance of the filters on basis of signal strength, speckle reduction, mean preservation and edge and feature preservation. In the paper, radiometric resolution, speckle index and mean preservation index will be used to analyze among the performance of the filters.
Performance Evaluation of 2D Adaptive Bilateral Filter For Removal of Noise F...CSCJournals
In this paper, we present the performance analysis of adaptive bilateral filter by pixel to noise ratio and mean square errors. It was evaluate changing the parameters of the adaptive filter half width values and standard deviations. In adaptive bilateral filter, the edge slope is enhanced by transforming the histogram via a range filter with adaptive offset and width. The variance of range filter can also be adaptive. The filter is applied to improve the sharpens of a gray level and color image by increasing the slope of the edges without producing overshoot or undershoots. The related graphs were plotted and the best filter parameters are obtained.
IMPROVEMENT OF BM3D ALGORITHM AND EMPLOYMENT TO SATELLITE AND CFA IMAGES DENO...ijistjournal
This paper proposes a new procedure in order to improve the performance of block matching and 3-D filtering (BM3D) image denoising algorithm. It is demonstrated that it is possible to achieve a better performance than that of BM3D algorithm in a variety of noise levels. This method changes BM3D algorithm parameter values according to noise level, removes prefiltering, which is used in high noise level; therefore Peak Signal-to-Noise Ratio (PSNR) and visual quality get improved, and BM3D complexities and processing time are reduced. This improved BM3D algorithm is extended and used to denoise satellite and color filter array (CFA) images. Output results show that the performance has upgraded in comparison with current methods of denoising satellite and CFA images. In this regard this algorithm is compared with Adaptive PCA algorithm, that has led to superior performance for denoising CFA images, on the subject of PSNR and visual quality. Also the processing time has decreased significantly.
Image Denoising Using Earth Mover's Distance and Local HistogramsCSCJournals
In this paper an adaptive range and domain filtering is presented. In the proposed method local histograms are computed to tune the range and domain extensions of bilateral filter. Noise histogram is estimated to measure the noise level at each pixel in the noisy image. The extensions of range and domain filters are determined based on pixel noise level. Experimental results show that the proposed method effectively removes the noise while preserves the details. The proposed method performs better than bilateral filter and restored test images have higher PSNR than those obtained by applying popular Bayesshrink wavelet denoising method.
Accelerated Joint Image Despeckling Algorithm in the Wavelet and Spatial DomainsCSCJournals
Noise is one of the most widespread problems present in nearly all imaging applications. In spite of the sophistication of the recently proposed methods, most denoising algorithms have not yet attained a desirable level of applicability. This paper proposes a two-stage algorithm for speckle noise reduction jointly in the wavelet and spatial domains. At the first stage, the optimal parameter value of the spatial speckle reduction filter is estimated, based on edge pixel statistics and noise variance. Then the optimized filter is used at the second stage to additionally smooth the approximation image of the wavelet sub-band. A complexity reduction algorithm for wavelet decomposition is also proposed. The obtained results are highly encouraging in terms of image quality which paves the way towards the reinforcement of the proposed algorithm for the performance enhancement of the Block Matching and 3D Filtering algorithm tackling multiplicative speckle noise.
OBIA on Coastal Landform Based on Structure Tensor csandit
This paper presents the OBIA method based on structure tensor to identify complex coastal
landforms. That is, develop Hessian matrix by Gabor filtering and calculate multiscale structure
tensor. Extract edge information of image from the trace of structure tensor and conduct
watershed segment of the image. Then, develop texons and create texton histogram. Finally,
obtain the final results by means of maximum likelihood classification with KL divergence as
the similarity measurement. The study findings show that structure tensor could obtain
multiscale and all-direction information with small data redundancy. Moreover, the method
described in the current paper has high classification accuracy
Analysis of Adaptive and Advanced Speckle Filters on SAR DataIOSRjournaljce
Synthetic Aperture RADAR(SAR) images get inherently affected by speckle noise which is multiplicative in nature. This noise affects the image spatial statistics and properties. Over the past several years, many SAR denoising algorithms have been developed to reduce speckle noise. Some of the standard speckle filters are Gamma MAP, Lee, Frost and Kuan filters. Further, these have also been modified to obtain better results after filtering, than their original counterparts. Apart from the standard speckle filters, advanced SAR filters like Block Matching 3 Dimensional (BM3D) are also present. In this paper several standard as well as advanced speckle filters have been analyzed and compared. For comparison, Quality Assessment has been performed where the filtered images are compared to each other using parameters like Radiometric Resolution and others. These parameters help to distinguish the performance of the filters on basis of signal strength, speckle reduction, mean preservation and edge and feature preservation. In the paper, radiometric resolution, speckle index and mean preservation index will be used to analyze among the performance of the filters.
Performance Evaluation of 2D Adaptive Bilateral Filter For Removal of Noise F...CSCJournals
In this paper, we present the performance analysis of adaptive bilateral filter by pixel to noise ratio and mean square errors. It was evaluate changing the parameters of the adaptive filter half width values and standard deviations. In adaptive bilateral filter, the edge slope is enhanced by transforming the histogram via a range filter with adaptive offset and width. The variance of range filter can also be adaptive. The filter is applied to improve the sharpens of a gray level and color image by increasing the slope of the edges without producing overshoot or undershoots. The related graphs were plotted and the best filter parameters are obtained.
IMPROVEMENT OF BM3D ALGORITHM AND EMPLOYMENT TO SATELLITE AND CFA IMAGES DENO...ijistjournal
This paper proposes a new procedure in order to improve the performance of block matching and 3-D filtering (BM3D) image denoising algorithm. It is demonstrated that it is possible to achieve a better performance than that of BM3D algorithm in a variety of noise levels. This method changes BM3D algorithm parameter values according to noise level, removes prefiltering, which is used in high noise level; therefore Peak Signal-to-Noise Ratio (PSNR) and visual quality get improved, and BM3D complexities and processing time are reduced. This improved BM3D algorithm is extended and used to denoise satellite and color filter array (CFA) images. Output results show that the performance has upgraded in comparison with current methods of denoising satellite and CFA images. In this regard this algorithm is compared with Adaptive PCA algorithm, that has led to superior performance for denoising CFA images, on the subject of PSNR and visual quality. Also the processing time has decreased significantly.
IMPROVEMENT OF BM3D ALGORITHM AND EMPLOYMENT TO SATELLITE AND CFA IMAGES DENO...ijistjournal
This paper proposes a new procedure in order to improve the performance of block matching and 3-D filtering (BM3D) image denoising algorithm. It is demonstrated that it is possible to achieve a better performance than that of BM3D algorithm in a variety of noise levels. This method changes BM3D algorithm parameter values according to noise level, removes prefiltering, which is used in high noise level; therefore Peak Signal-to-Noise Ratio (PSNR) and visual quality get improved, and BM3D complexities and processing time are reduced. This improved BM3D algorithm is extended and used to denoise satellite and color filter array (CFA) images. Output results show that the performance has upgraded in comparison with current methods of denoising satellite and CFA images. In this regard this algorithm is compared with Adaptive PCA algorithm, that has led to superior performance for denoising CFA images, on the subject of PSNR and visual quality. Also the processing time has decreased significantly.
Boosting CED Using Robust Orientation Estimationijma
n this paper, Coherence Enhancement Diffusion (CED) is boosted feeding external orientation using new
robust orientation estimation. In CED, proper scale selection is very important as the gradient vector at
that scale reflects the orientation of local ridge. For this purpose a new scheme is proposed in which pre
calculated orientation, by using local and integration scales. From the experiments it is found the proposed
scheme is working much better in noisy environment as compared to the traditional Coherence
Enhancement Diffusion
NMS and Thresholding Architecture used for FPGA based Canny Edge Detector for...idescitation
In this paper, an architecture designed for Non-
Maximal Suppression used in Canny edge detection algorithm
is presented in order to reduce memory requirements
significantly. The architecture also achieves decreased latency
and increased throughput with no loss in edge detection. The
new algorithm used has a low-complexity 8-bin non-uniform
gradient magnitude histogram to compute block-based
hysteresis thresholds that are used by the Canny edge detector.
Furthermore, the hardware architecture of the proposed
algorithm is presented in this paper and the architecture is
synthesized on the Xilinx Virtex 5 FPGA. The design
development is done in VHDL and simulated results are
obtained using modelsim 6.3 with Xilinx 12.2.
Boosting ced using robust orientation estimationijma
In this paper, Coherence Enhancement Diffusion (CED) is boosted feeding external orientation using new
robust orientation estimation. In CED, proper scale selection is very important as the gradient vector at
that scale reflects the orientation of local ridge. For this purpose a new scheme is proposed in which pre
calculated orientation, by using local and integration scales. From the experiments it is found the proposed
scheme is working much better in noisy environment as compared to the traditional Coherence
Enhancement Diffusion
Modified adaptive bilateral filter for image contrast enhancementeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IMAGE DENOISING BY MEDIAN FILTER IN WAVELET DOMAINijma
The details of an image with noise may be restored by removing noise through a suitable image de-noising
method. In this research, a new method of image de-noising based on using median filter (MF) in the
wavelet domain is proposed and tested. Various types of wavelet transform filters are used in conjunction
with median filter in experimenting with the proposed approach in order to obtain better results for image
de-noising process, and, consequently to select the best suited filter. Wavelet transform working on the
frequencies of sub-bands split from an image is a powerful method for analysis of images. According to this
experimental work, the proposed method presents better results than using only wavelet transform or
median filter alone. The MSE and PSNR values are used for measuring the improvement in de-noised
images.
International Journal of Engineering Research and Applications (IJERA) aims to cover the latest outstanding developments in the field of all Engineering Technologies & science.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
ER Publication,
IJETR, IJMCTR,
Journals,
International Journals,
High Impact Journals,
Monthly Journal,
Good quality Journals,
Research,
Research Papers,
Research Article,
Free Journals, Open access Journals,
erpublication.org,
Engineering Journal,
Science Journals,
EDGE DETECTION IN RADAR IMAGES USING WEIBULL DISTRIBUTIONcsitconf
Radar images can reveal information about the shape of the surface terrain as well as its
physical and biophysical properties. Radar images have long been used in geological studies to
map structural features that are revealed by the shape of the landscape. Radar imagery also has
applications in vegetation and crop type mapping, landscape ecology, hydrology, and
volcanology. Image processing is using for detecting for objects in radar images. Edge
detection; which is a method of determining the discontinuities in gray level images; is a very
important initial step in Image processing. Many classical edge detectors have been developed
over time. Some of the well-known edge detection operators based on the first derivative of the
image are Roberts, Prewitt, Sobel which is traditionally implemented by convolving the image
with masks. Also Gaussian distribution has been used to build masks for the first and second
derivative. However, this distribution has limit to only symmetric shape. This paper will use to
construct the masks, the Weibull distribution which was more general than Gaussian because it
has symmetric and asymmetric shape. The constructed masks are applied to images and we
obtained good results.
EDGE DETECTION IN RADAR IMAGES USING WEIBULL DISTRIBUTIONcscpconf
Radar images can reveal information about the shape of the surface terrain as well as its physical and biophysical properties. Radar images have long been used in geological studies to
map structural features that are revealed by the shape of the landscape. Radar imagery also has applications in vegetation and crop type mapping, landscape ecology, hydrology, and
volcanology. Image processing is using for detecting for objects in radar images. Edge detection; which is a method of determining the discontinuities in gray level images; is a very
important initial step in Image processing. Many classical edge detectors have been developed over time. Some of the well-known edge detection operators based on the first derivative of the image are Roberts, Prewitt, Sobel which is traditionally implemented by convolving the image with masks. Also Gaussian distribution has been used to build masks for the first and second derivative. However, this distribution has limit to only symmetric shape. This paper will use to construct the masks, the Weibull distribution which was more general than Gaussian because it has symmetric and asymmetric shape. The constructed masks are applied to images and we obtained good results.
International Journal of Engineering Research and Applications (IJERA) aims to cover the latest outstanding developments in the field of all Engineering Technologies & science.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
Edge detection is the name for a set of mathematical methods which aim at identifying points in a digital image at which the image brightness changes sharply or, more formally, has discontinuities.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
IMPROVEMENT OF BM3D ALGORITHM AND EMPLOYMENT TO SATELLITE AND CFA IMAGES DENO...ijistjournal
This paper proposes a new procedure in order to improve the performance of block matching and 3-D filtering (BM3D) image denoising algorithm. It is demonstrated that it is possible to achieve a better performance than that of BM3D algorithm in a variety of noise levels. This method changes BM3D algorithm parameter values according to noise level, removes prefiltering, which is used in high noise level; therefore Peak Signal-to-Noise Ratio (PSNR) and visual quality get improved, and BM3D complexities and processing time are reduced. This improved BM3D algorithm is extended and used to denoise satellite and color filter array (CFA) images. Output results show that the performance has upgraded in comparison with current methods of denoising satellite and CFA images. In this regard this algorithm is compared with Adaptive PCA algorithm, that has led to superior performance for denoising CFA images, on the subject of PSNR and visual quality. Also the processing time has decreased significantly.
Boosting CED Using Robust Orientation Estimationijma
n this paper, Coherence Enhancement Diffusion (CED) is boosted feeding external orientation using new
robust orientation estimation. In CED, proper scale selection is very important as the gradient vector at
that scale reflects the orientation of local ridge. For this purpose a new scheme is proposed in which pre
calculated orientation, by using local and integration scales. From the experiments it is found the proposed
scheme is working much better in noisy environment as compared to the traditional Coherence
Enhancement Diffusion
NMS and Thresholding Architecture used for FPGA based Canny Edge Detector for...idescitation
In this paper, an architecture designed for Non-
Maximal Suppression used in Canny edge detection algorithm
is presented in order to reduce memory requirements
significantly. The architecture also achieves decreased latency
and increased throughput with no loss in edge detection. The
new algorithm used has a low-complexity 8-bin non-uniform
gradient magnitude histogram to compute block-based
hysteresis thresholds that are used by the Canny edge detector.
Furthermore, the hardware architecture of the proposed
algorithm is presented in this paper and the architecture is
synthesized on the Xilinx Virtex 5 FPGA. The design
development is done in VHDL and simulated results are
obtained using modelsim 6.3 with Xilinx 12.2.
Boosting ced using robust orientation estimationijma
In this paper, Coherence Enhancement Diffusion (CED) is boosted feeding external orientation using new
robust orientation estimation. In CED, proper scale selection is very important as the gradient vector at
that scale reflects the orientation of local ridge. For this purpose a new scheme is proposed in which pre
calculated orientation, by using local and integration scales. From the experiments it is found the proposed
scheme is working much better in noisy environment as compared to the traditional Coherence
Enhancement Diffusion
Modified adaptive bilateral filter for image contrast enhancementeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IMAGE DENOISING BY MEDIAN FILTER IN WAVELET DOMAINijma
The details of an image with noise may be restored by removing noise through a suitable image de-noising
method. In this research, a new method of image de-noising based on using median filter (MF) in the
wavelet domain is proposed and tested. Various types of wavelet transform filters are used in conjunction
with median filter in experimenting with the proposed approach in order to obtain better results for image
de-noising process, and, consequently to select the best suited filter. Wavelet transform working on the
frequencies of sub-bands split from an image is a powerful method for analysis of images. According to this
experimental work, the proposed method presents better results than using only wavelet transform or
median filter alone. The MSE and PSNR values are used for measuring the improvement in de-noised
images.
International Journal of Engineering Research and Applications (IJERA) aims to cover the latest outstanding developments in the field of all Engineering Technologies & science.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
ER Publication,
IJETR, IJMCTR,
Journals,
International Journals,
High Impact Journals,
Monthly Journal,
Good quality Journals,
Research,
Research Papers,
Research Article,
Free Journals, Open access Journals,
erpublication.org,
Engineering Journal,
Science Journals,
EDGE DETECTION IN RADAR IMAGES USING WEIBULL DISTRIBUTIONcsitconf
Radar images can reveal information about the shape of the surface terrain as well as its
physical and biophysical properties. Radar images have long been used in geological studies to
map structural features that are revealed by the shape of the landscape. Radar imagery also has
applications in vegetation and crop type mapping, landscape ecology, hydrology, and
volcanology. Image processing is using for detecting for objects in radar images. Edge
detection; which is a method of determining the discontinuities in gray level images; is a very
important initial step in Image processing. Many classical edge detectors have been developed
over time. Some of the well-known edge detection operators based on the first derivative of the
image are Roberts, Prewitt, Sobel which is traditionally implemented by convolving the image
with masks. Also Gaussian distribution has been used to build masks for the first and second
derivative. However, this distribution has limit to only symmetric shape. This paper will use to
construct the masks, the Weibull distribution which was more general than Gaussian because it
has symmetric and asymmetric shape. The constructed masks are applied to images and we
obtained good results.
EDGE DETECTION IN RADAR IMAGES USING WEIBULL DISTRIBUTIONcscpconf
Radar images can reveal information about the shape of the surface terrain as well as its physical and biophysical properties. Radar images have long been used in geological studies to
map structural features that are revealed by the shape of the landscape. Radar imagery also has applications in vegetation and crop type mapping, landscape ecology, hydrology, and
volcanology. Image processing is using for detecting for objects in radar images. Edge detection; which is a method of determining the discontinuities in gray level images; is a very
important initial step in Image processing. Many classical edge detectors have been developed over time. Some of the well-known edge detection operators based on the first derivative of the image are Roberts, Prewitt, Sobel which is traditionally implemented by convolving the image with masks. Also Gaussian distribution has been used to build masks for the first and second derivative. However, this distribution has limit to only symmetric shape. This paper will use to construct the masks, the Weibull distribution which was more general than Gaussian because it has symmetric and asymmetric shape. The constructed masks are applied to images and we obtained good results.
International Journal of Engineering Research and Applications (IJERA) aims to cover the latest outstanding developments in the field of all Engineering Technologies & science.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
Edge detection is the name for a set of mathematical methods which aim at identifying points in a digital image at which the image brightness changes sharply or, more formally, has discontinuities.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...
channel_mzhazbay.pdf
1. Extraction of channel structure from a depth slice
of 3-D seismic volume using bilateral filter and
edge detection algorithm∗
Maksat Zhazbayev†
‡
1 Abstract
The bilateral filter is a nonlinear filter that smoothes a signal by spacial
filtering and attempts to preserve edges. This is accomplished by taking into
account both the spatial proximity and similarity in intensity. The two Gaussian
functions are used such as one that takes the physical distance between two
pixels as an input, and other one that takes the absolute difference in intensity
as its input. The bilateral filter has proven to be an effective image denoising
technique that can be used in various ways according to channel edge extraction
from a depth slice of 3-D seismic volume. The selection of the filter parameters
is an important issue during the application of bilateral filter. In this paper,
a synopsis on bilateral filtering operations and its applications are discussed in
particular for image denoising.
2 Introduction
In the Figure 1, a depth slice from 3-D seismic volume is illustrated on the
left. It has a feature like a channel structure that needs to be extracted using
Canny’s (1986) edge detection algorithm. In the right plot of Figure 1, edge
detector picks areas of high gradient that seem to be aligned along an edge.
The result is not too clear because it is affected by some random fluctuations
in seismic amplitudes. In this paper, I attempt to achieve a better result in
channel extraction by smoothing while keeping the edges of the structure.
To introduce bilateral filtering, I begin by defining Gaussian convolution.
Conventional smoothing as Gaussian filter removes amplitude fluctuations but
at the cost of broadening the channel that makes it unreliable. After, I proceed
to use the bilateral filter that is also a weighted-average filter. The filtered
∗Completed for the Certificate in Scientific Computation
†Maksat Zhazbayev - B.S. in Geophysics in Jackson School of Geosciences
‡Special Thanks to my advisor Sergey Fomel in Jackson School of Geosciences and Bureau
of Economic Geology in University of Texas at Austin
1
2. (a) (b)
Figure 1: (a) Depth slice from 3-D seismic and (b) output of edge detection
output is computed at each pixel as the average of neighboring pixels that is
weighted by the Gaussian of spatial and intensity distance. The bilateral filter
performs in the range of the image what traditional filters do in its domain since
the idea of bilateral filter is to smooth the image while preserving the edges.
Depending on the image intensity, range filtering is defined in order to average
image values with weights that decay with dissimilarity. Afterwards, the range
and domain kernels are combined where if one of the weights is close to zero, a
limited smoothing occurs.
3 Materials and Methods
First of all, I used Gaussian blurring that is the simplest way for smoothing to
remove random amplitude fluctuations from a depth slice of 3-D seismic volume.
The main component is the convolution by a kernel, and each output pixel value
is weighted sum of its neighbors in input image. Gaussian Convolutions is given
by:
GC[I]p =
X
q∈S
Gσ(||p - q||)Iq, (1)
where Gσ(x) indicates 2D Gaussian Kernel that is defined as:
Gσ(x) =
1
√
2πσ2
exp(
−x2
2σ2
) (2)
The weight for pixel q is defined by Gaussian Gσ(||p - q||), where parameter σ
defines the size of the neighborhood. This influence is based on spatial distance
between pixels and not their values.
Furthermore, I define the bilateral filter to smooth the image while preserving
its edges. The name of bilateral filter indicates that the kernel of the filter is a
combination of the two filter kernels such as one is a function of input image’s
spatial domain, and the other is a function of its range. The most common
2
3. way of doing this is by using Gaussian functions that give values according to a
normal distribution and has probability density function:
g(x) =
1
√
2πσ2
exp(
−x2
2σ2
) (3)
This weighted value is then multiplied by the initial value of the input pixel
and summed over a neighborhood. Then, it is normalized by dividing by the
sum of the weighted values. For a neighborhood around Iin(i, j) denoted by Ω,
this gives:
Iout(i, j) =
P
p∈Ω gσs
(|p − (i, j)|)gσr
(|Ip − Iin(i, j)|)Ip
P
p∈Ω gσs
(|p − (i, j)|)gσr
(|Ip − Iin(i, j)|)
(4)
In the equation (4), σr and σs are the range and spatial parameters respec-
tively. Changing these parameters affect the distribution of values, potentially
giving more or less weight to pixels further away. As the two values of Gaus-
sian function are multiplied, the smoothing occurs when the pixels are close or
similar to each other. Consequently, when one of the values is close to zero,
the whole term becomes insignificant. This essentially means that pixels that
are similar but far away, or close but very different, have a little effect on the
output.
The bilateral filter was implemented both for 1-D and 2-D in Python and C
respectively. A Gaussian function g(x) equivalent to in equation (3) is returned
by the function gaussian(int x, int sigma). The distance difference or domain
distance will be the same regardless of what pixel it is being applied to, and is
returned by the function distance(int x, int y, int i, int j). Also, the intensity
difference or range distance Gaussian filter is defined in the body of the function
apply bilateral filter(float source, float filtered image, int x, int y, int r1, int r2,
int sig i, int sig s, int n1, int n2), where result will vary as it depends on the
intensity of the pixel it is being applied to and the intensities of the neighboring
pixels. This function apply bilateral filter() basically applies the element wise
multiplication of these two different masks at every pixel p that is eventually
weighted by the sum of its own components.
4 Results
The Gaussian convolution is independent of the image content. Therefore, a
pixel has an influence on another one depending only on their distance in the
image. For example, a bright pixel would have an influence over a dark pixel
even though these pixels have a different values.
Just like Gaussian convolution, the bilateral filter is described as weighted
average of pixels in the neighborhood. The difference is bilateral filter accounts
for the difference in intensity values with the neighbors to preserve the edges.
The amount of filtering is specified by the parameters σs and σr. For example,
the Figure-3 illustrates gaussian smoothing and bilateral filtering for a synthetic
3
4. example in Figure 2, of a blocky signal containing some random noise. In Figure
3 (a) the random noise gets smoothed at the cost of blurring the edges. The
Figure 3 (b) simply explains how effective bilateral filter would be when applied
to a depth slice from a 3-D seismic volume that has a channel structure that
needs to be extracted.
Figure 2: 1-D synthetic blocky signal contaminated with random noise
As shown in Figure-5, when gaussian smoothing is applied to a depth slice of
3-D seismic volume, random fluctuations are removed by gaussian smoothing.
However, it also broadens the channel and makes the channel edge detection
unreliable. Whereas, bilateral filtering provides a better solution by keeping the
shape of the channel structure and smoothes around it as shown in Figure-6.
Gaussian linear filtering blurs out the image because pixels across discon-
tinuities are averaged together and the edges get lost with high σ since the
averaging is performed over a bigger area.
(a) (b)
Figure 3: (a) Gaussian smoothing and (b) Bilateral filtering
4
5. (a) (b)
Figure 4: (a) Original depth slice from 3-D seismic volume and (b) output of
edge detection
(a) (b)
Figure 5: (a) Gaussian smoothing of the depth slice and (b) output of edge
detection
(a) (b)
Figure 6: (a) Bilateral filtering of the depth slice and (b) output of edge detection
5
6. (a)
(b)
Figure 7: (a) Original Image 512x512 and (b) Bilateral filtering with σs=16 and
σr=10 over 10 iterations
In addition, the bilateral filter can be iterated meaning the filter can be ap-
plied a multiple times to previous result. Applying the filter over a big number
of iterations produces a cartoonish image as in Figure 7.
5 Discussion
The gaussian filter is simpler and provides an introduction of local averaging
concept. It removes the random noise by smoothing but at the expense of
having the edges blurred. Whereas, the bilateral filter is a simple and effective
way to smooth an image while preserving discontinuities, and separate image
structures as I illustrated in the example with the channel. Manipulating with
parameters of bilateral filter provides a lot more diverse set of applications as
6
7. in Figure 8. Overall, the bilateral filter is an expensive algorithm in particular
if a large neighborhood size is defined, and on top of it needs a calculation of
the two weights, and a normalization step as well.
7
9. 6 Acknowledgements
I would like to thank Sergey Fomel for his advices and help that I received
throughout this project. Without his help, my final result wouldn’t be nearly
what it is.
9
10. References
[1] Canny, J., 1986, A computational approach to edge detection: IEEE Trans.
Pattern Analysis and Machine Intelligence, 8, 679-714.
[2] Gilboa, G., and S. Osher, 2008, Nonlocal operators with applications to
image processing: Multiscale Model & Simulation, 7, 1005-1028.
[3] Fomel, S., and G. Hennenfent, 2007, Reproducible computational experi-
ments using SCons: 32nd International Conference on Acoustics, Speech,
and Signal Processing (ICASSP), IEEE, 1257-1260.
[4] Tomasi, C., and R. Manduchi, 1998, Bilateral filtering for gray and color
images: Proceedings of IEEE International Conference on Computer Vision,
IEEE, 836-846.
10
11. 7 Appendix A
1 d b i l a t e r a l . py
#!/ usr / bin /env python
import m8r as s f
import numpy as np
import math
input = s f . Input ()
output = s f . Output ()
par = s f . Par ()
n1 = input . int (” n1 ”)
#n2 = input . int (” n2 ”)
#sigmaD = 5
#sigmaR = 5
sigmaD = par . f l o a t (” sigmaD ”)
sigmaR = par . f l o a t (” sigmaR ”)
i t e r a t i o n s = par . int (” i t e r a t i o n s ”)
I = np . zeros (n1 , ’ f ’ )
input . read ( I )
sz = 40
I = np . pad ( I , ( sz , sz ) , ’ constant ’ , constant values =0)
w = np . zeros ( I . shape , ’ f ’ )
num = np . zeros ( I . shape , ’ f ’ )
Id = np . zeros ( I . shape , ’ f ’ )
f or i t e r a t i o n in range ( i t e r a t i o n s ) :
f or i in range ( sz , len ( I )−sz ) :
sumW = 0
sumNum = 0
f or k in range ( i−sz , i+sz ) :
W = np . exp ( −(i−k) ∗∗2 / (2∗ sigmaD∗∗2) − ( I [ i
]− I [ k ] ) ∗∗2 / (2∗ sigmaR ∗∗2) )
sumW += W
sumNum += I [ k ] ∗ W
I [ i ] = sumNum / sumW
output . write ( I [ sz : len ( I )−sz ] )
11
12. 8 Appendix B
2 d b i l a t e r a l . c
#include <stdio . h>
#include <math . h>
#include <r s f . h>
f l o a t distance ( int x , int y , int i , int j ) ;
f l o a t gaussian ( int x , f l o a t sigma ) ;
void a p p l y b i l a t e r a l f i l t e r ( f l o a t ∗∗ source , f l o a t ∗∗
filtered image , int x , int y , int r1 , int r2 , f l o a t
sigma i , f l o a t sigma s , int n1 , int n2 ) ;
void a p p l y b i l a t e r a l f i l t e r o w n ( f l o a t ∗∗ source , f l o a t ∗∗
filtered image , int r1 , int r2 , f l o a t sigma i , f l o a t
sigma s , int n1 , int n2 , int i t e r a t i o n s ) ;
int main ( int argc , char ∗ argv [ ] )
{
int n1 , n2 , n12 , r1 , r2 , i t e r a t i o n s ;
f l o a t ∗∗ dinp=NULL, ∗∗dout=NULL;
f l o a t a1 , a2 ;
s f f i l e inp , out ;
s f i n i t ( argc , argv ) ;
inp = s f i n p u t (” in ”) ;
out = sf output (” out ”) ;
i f (SF FLOAT != s f g e t t y p e ( inp ) ) s f e r r o r (”Need f l o a t
input ”) ;
i f ( ! s f h i s t i n t ( inp ,” n1”,&n1 ) ) s f e r r o r (”No n1= in
input ”) ;
i f ( ! s f h i s t i n t ( inp ,” n2”,&n2 ) ) s f e r r o r (”No n2= in
input ”) ;
n12 = n1∗n2 ;
i f ( ! s f g e t i n t (” r1 ”,& r1 ) ) r1 =1; /∗ v e r t i c a l smoothing
radius ∗/
i f ( ! s f g e t i n t (” r2 ”,& r2 ) ) r2 =1; /∗ horizontal
12
13. smoothing radius ∗/
i f ( ! s f g e t i n t (” i t e r a t i o n s ”,& i t e r a t i o n s ) ) i t e r a t i o n s
=1; /∗ i t e r a t i o n s ∗/
i f ( ! s f g e t f l o a t (” a1”,&a1 ) ) a1=0.0 f ; /∗ sigma i
s p a t i a l ∗/
i f ( ! s f g e t f l o a t (” a2”,&a2 ) ) a2=a1 ; /∗ sigma s range
∗/
dinp = s f f l o a t a l l o c 2 (n1 , n2 ) ;
dout = s f f l o a t a l l o c 2 (n1 , n2 ) ;
s f f l o a t r e a d ( dinp [ 0 ] , n12 , inp ) ;
a p p l y b i l a t e r a l f i l t e r o w n ( dinp , dout , r1 , r2 , a1 , a2
, n1 , n2 , i t e r a t i o n s ) ;
// s f f l o a t w r i t e ( dout [ 0 ] , n12 , out ) ;
s f f l o a t w r i t e ( dinp [ 0 ] , n12 , out ) ;
}
f l o a t distance ( int x , int y , int i , int j ) {
f l o a t r e s u l t = (x−i ) ∗(x−i ) + (y−j ) ∗(y−j ) ;
return r e s u l t ;
}
f l o a t gaussian ( int x , f l o a t sigma ) {
f l o a t pi = 3 . 1 4 ;
f l o a t r e s u l t = ( 1 . / pow(2 ∗ pi ∗ ( sigma ∗ sigma )
, 0 .5 ) ) ∗ expf (−(x∗x) / (2∗ sigma∗sigma ) ) ;
return r e s u l t ;
}
void a p p l y b i l a t e r a l f i l t e r ( f l o a t ∗∗ source , f l o a t ∗∗
filtered image , int x , int y , int r1 , int r2 , f l o a t
sigma i , f l o a t sigma s , int n1 , int n2 ) {
f l o a t i f i l t e r e d = 0;
f l o a t Wp = 0;
int k1 , k2 ;
f l o a t gi , gs ;
f l o a t w;
int neighbour x , neighbour y ;
f l o a t neighbour , p i x e l ;
f or ( k1=−r1 ; k1<r1 ; k1++){
13
14. neighbour x = x+k1 ;
i f ( neighbour x < 0 | | neighbour x >= n1 )
continue ;
f or ( k2=−r2 ; k2<r2 ; k2++){
neighbour y = y+k2 ;
i f ( neighbour y < 0 | | neighbour y >= n2 )
continue ;
// neighbour = ∗( source+neighbour x ∗n2+
neighbour y ) ;
// p i x e l = ∗( source+x∗n2+y) ;
neighbour = source [ neighbour x ] [ neighbour y ] ;
p i x e l = source [ x ] [ y ] ;
gi = gaussian ( distance ( neighbour x ,
neighbour y , x , y) , sigma i ) ;
gs = gaussian ( neighbour − pixel , sigma s ) ;
w = gi ∗ gs ;
Wp += w;
i f i l t e r e d += neighbour ∗ w;
// p r i n t f (”% f n and %f n” , gi , gs ) ;
}
}
i f i l t e r e d = i f i l t e r e d / Wp;
// f i l t e r e d i m a g e [ x ] [ y ] = i f i l t e r e d ;
source [ x ] [ y ] = i f i l t e r e d ;
//∗( f i l t e r e d i m a g e+x∗n2+y) = i f i l t e r e d ;
// p r i n t f (”% f n” , i f i l t e r e d ) ;
// p r i n t f (”% f n” , ∗( source+x∗n2+y) ) ;
}
void a p p l y b i l a t e r a l f i l t e r o w n ( f l o a t ∗∗ source , f l o a t ∗∗
filtered image , int r1 , int r2 , f l o a t sigma i , f l o a t
sigma s , int n1 , int n2 , int i t e r a t i o n s ) {
int x , y , i t e r ;
f or ( i t e r =0; i t e r < i t e r a t i o n s ; i t e r++){
f or (x=0; x<n1 ; x++){
f or (y=0; y<n2 ; y++){
a p p l y b i l a t e r a l f i l t e r ( source ,
filtered image , x , y , r1 , r2 , sigma i ,
sigma s , n1 , n2 ) ;
// p r i n t f (”% f n” ,∗( f i l t e r e d i m a g e+x∗n2+y) )
;
}
}
}
}
14
15. 9 Appendix C
SConstruct
from r s f . proj import∗
prog = Program ( ’2 d b i l a t e r a l . c ’ )
b i l a t e r a l = s t r ( prog [ 0 ] )
#### 1−D NOISY SIGNAL BILATERAL FILTER
####
Flow ( ’1 dsignal ’ , None , ’ spike nsp=3 n1=101 k1=26 ,51 ,76 mag
=−1,2,−1 d1=1 | causint | noise var =0.001 ’)
Result ( ’1 dsignal ’ , ’ dots t i t l e =”1−D blocky s i g n a l w/
random noise ” ’)
Flow ( ’1 d b i l a t e r a l ’ , ’ 1 dsignal 1 d b i l a t e r a l . py ’ , ’ . / ${
SOURCES[ 1 ] } sigmaD=10 sigmaR=0.3 i t e r a t i o n s =1 ’)
Result ( ’1 d b i l a t e r a l ’ , ’ dots t i t l e =”B i l a t e r a l f i l t e r ” ’)
Flow ( ’1 d gaussian ’ , ’ 1 dsignal ’ , ’ gaussmooth rect =10 ’)
Result ( ’1 d gaussian ’ , ’ dots wanttitle=n ’)
#### HORIZON
−Channel ####
Fetch ( ’ horizon . asc ’ , ’ hall ’ )
Flow ( ’ horizon ’ , ’ horizon . asc ’ ,
’ ’ ’
echo in=$SOURCE data format=a s c i i f l o a t n1=3 n2
=57036 |
dd form=native | window n1=1 f1=−1 |
put
n1=196 o1=33.139 d1=0.01 label1=y unit1=km
n2=196 o2=35.031 d2=0.01 label2=x unit2=km
’ ’ ’)
Result ( ’ horizon ’ , ’ grey color=j bias=0 yreverse=n t i t l e=
Original ’ )
Flow ( ’ edgehorizon ’ , ’ horizon ’ , ’ canny max=98 | dd type=
float ’ )
Result ( ’ edgehorizon ’ , ’ grey color=j yreverse=n label1=”y (
km) ” label2=”x (km) ” bias=0 wanttitle=n ’ )
#### Applying Gaussian smoothing ####
Flow ( ’ horizong ’ , ’ horizon ’ , ’ smooth rect1=20 rect2 =20 ’)
15
16. Result ( ’ horizong ’ , ’ grey color=j yreverse=n label1=”y (km)
” label2=”x (km) ” bias=0 wanttitle=n ’ )
Flow ( ’ edgehorizong ’ , ’ horizong ’ , ’ canny max=98 | dd type=
float ’ )
Result ( ’ edgehorizong ’ , ’ grey color=j yreverse=n label1=”y
(km) ” label2=”x (km) ” bias=0 wanttitle=n ’ )
#### Applying 2−D B i l a t e r a l F i l t e r ####
Flow ( ’ horizonb ’ , [ ’ horizon ’ , b i l a t e r a l ] , ’./ ${SOURCES[ 1 ] }
r1=25 r2=25 a1=4 a2=7 i t e r a t i o n s =1 ’)
Result ( ’ horizonb ’ , ’ grey color=j yreverse=n wanttitle=n ’ )
Flow ( ’ edgehorizonb ’ , ’ horizonb ’ , ’ canny max=98 | dd type=
float ’ )
Result ( ’ edgehorizonb ’ , ’ grey color=j yreverse=n wanttitle=
n ’)
#### MONA 2D BILAT ####
Flow ( ’mona ’ , ’ mona . img ’ , ’ echo in=$SOURCE n1=512 n2=512 d1
=1 d2=1 o1=0 o2=0 data format=native uchar | dd type=
float ’ )
Result ( ’mona ’ , ’ grey wanttitle=n color=b transp=n ’ )
Flow ( ’monab ’ , [ ’ mona ’ , b i l a t e r a l ] , ’./ ${SOURCES[ 1 ] } r1=10
r2=10 a1=16 a2=10 i t e r a t i o n s =10 ’)
Result ( ’monab ’ , ’ grey wanttitle=n color=b transp=n ’ )
Flow ( ’ monabdiff ’ , ’ monab mona ’ , ’ d i f f e r e n c e subtracter=${
SOURCES[ 1 ] } ’ )
Result ( ’ monabdiff ’ , ’ grey transp=n color=b t i t l e=Mona ’ )
f or i in range (4 ,16 ,4) :
f or j in range (1 ,10 ,3) :
monabb = ’mona−’ + s t r ( i ) + ’−’ + s t r ( j )
Flow (monabb , [ ’ mona ’ , b i l a t e r a l ] , ’./ ${
SOURCES[ 1 ] } r1=10 r2=10 a1=%d a2=%d
i t e r a t i o n s =10’ % ( i , j ) )
Result (monabb , ’ grey transp=n color=b
t i t l e=%s ’%(monabb) )
End()
16
17. 10 Reflection
Above methods are implemented with Madagascar in languages C and Python.
Madagascar is an open-source software package for multidimensional data anal-
ysis and reproducible computational experiments in geoscience.
During this project I learned a development kit for C, and Python in Mada-
gascar environment. Also, I picked up a new skills such as writing scientific
publications based on L
A
TEX, and reproducing numerical experiments based on
SCons.
17