This document presents a method for image upscaling using a fuzzy ARTMAP neural network. It begins with an introduction to image upscaling and interpolation techniques. It then provides background on ARTMAP neural networks and fuzzy logic. The proposed method uses a linear interpolation algorithm trained with an ARTMAP network. Results show the method performs better than nearest neighbor interpolation in terms of peak signal-to-noise ratio, mean squared error, and structural similarity, though not as high as bicubic interpolation. Overall, the fuzzy ARTMAP network provides an effective way to perform image upscaling with fewer artifacts than traditional methods.
A NOVEL IMAGE STEGANOGRAPHY APPROACH USING MULTI-LAYERS DCT FEATURES BASED ON...ijma
Steganography is the science of hidden data in the cover image without any updating of the cover image.
The recent research of the steganography is significantly used to hide large amount of information within
an image and/or audio files. This paper proposed a new novel approach for hiding the data of secret image
using Discrete Cosine Transform (DCT) features based on linear Support Vector Machine (SVM)
classifier. The DCT features are used to decrease the image redundant information. Moreover, DCT is
used to embed the secrete message based on the least significant bits of the RGB. Each bit in the cover
image is changed only to the extent that is not seen by the eyes of human. The SVM used as a classifier to
speed up the hiding process via the DCT features. The proposed method is implemented and the results
show significant improvements. In addition, the performance analysis is calculated based on the
parameters MSE, PSNR, NC, processing time, capacity, and robustness.
A NOVEL IMAGE STEGANOGRAPHY APPROACH USING MULTI-LAYERS DCT FEATURES BASED ON...ijma
Steganography is the science of hidden data in the cover image without any updating of the cover image.
The recent research of the steganography is significantly used to hide large amount of information within
an image and/or audio files. This paper proposed a new novel approach for hiding the data of secret image
using Discrete Cosine Transform (DCT) features based on linear Support Vector Machine (SVM)
classifier. The DCT features are used to decrease the image redundant information. Moreover, DCT is
used to embed the secrete message based on the least significant bits of the RGB. Each bit in the cover
image is changed only to the extent that is not seen by the eyes of human. The SVM used as a classifier to
speed up the hiding process via the DCT features. The proposed method is implemented and the results
show significant improvements. In addition, the performance analysis is calculated based on the
parameters MSE, PSNR, NC, processing time, capacity, and robustness.
High Speed Data Exchange Algorithm in Telemedicine with Wavelet based on 4D M...Dr. Amarjeet Singh
Existing Medical imaging techniques such as fMRI, positron emission tomography (PET), dynamic 3D ultrasound and dynamic computerized tomography yield large amounts of four-dimensional sets. 4D medical data sets are the series of volumetric images netted in time, large in size and demand a great of assets for storage and transmission. Here, in this paper, we present a method wherein 3D image is taken and Discrete Wavelet Transform(DWT) and Dual-Tree Complex Wavelet Transform(DTCWT) techniques are applied separately on it and the image is split into sub-bands. The encoding and decoding are done using 3D-SPIHT, at different bit per pixels(bpp). The reconstructed image is synthesized using Inverse DWT technique. The quality of the compressed image has been evaluated using some factors such as Mean Square Error(MSE) and Peak-Signal to Noise Ratio (PSNR).
ON THE IMAGE QUALITY AND ENCODING TIMES OF LSB, MSB AND COMBINED LSB-MSBijcsit
The Least Significant Bit (LSB) algorithm and the Most Significant Bit (MSB) algorithm are stenography algorithms with each one having its demerits. This work therefore proposed a Hybrid approach and compared its efficiency with LSB and MSB algorithms. The Least Significant Bit (LSB) and Most
Significant Bit (MSB) techniques were combined in the proposed algorithm. Two bits (the least significant bit and the most significant bit) of the cover images were replaced with a secret message. Comparisons were made based on Mean-Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR) and the encoding time between the proposed algorithm, LSB and MSB after embedding in digital images. The combined
technique produced a stego-image with minimal distortion in image quality than MSB technique independent of the nature of data that was hidden. However, LSB algorithm produced the best stego-image quality. Large cover images however made the combined algorithm’s quality better improved. The combined algorithm had lesser time of image and text encoding. Therefore, a trade-off exists between the encoding time and the quality of stego-image as demonstrated in this work.
Image Encryption Based on Pixel Permutation and Text Based Pixel Substitutionijsrd.com
Digital image Encryption techniques play a very important role to prevent image from unauthorized access. There are many types of methods available that can do Image Encryption, and the majority of them are scrambling algorithms based on pixel shuffling, which cannot change the histogram of an image. Hence, their security performances are not good. The encryption method that combines the pixel exchanging and gray level changing can handles reach a good chaotic effect. In this paper we focus on an image encryption technique based on pixel wise shuffling with the help of skew tent map and text based pixel substitution. The PSNR, NPCR and CC obtained by our technique shows that the proposed technique gives better result than the existing techniques.
A simple framework for contrastive learning of visual representationsDevansh16
Link: https://machine-learning-made-simple.medium.com/learnings-from-simclr-a-framework-contrastive-learning-for-visual-representations-6c145a5d8e99
If you'd like to discuss something, text me on LinkedIn, IG, or Twitter. To support me, please use my referral link to Robinhood. It's completely free, and we both get a free stock. Not using it is literally losing out on free money.
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This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank. In order to understand what enables the contrastive prediction tasks to learn useful representations, we systematically study the major components of our framework. We show that (1) composition of data augmentations plays a critical role in defining effective predictive tasks, (2) introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and (3) contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning. By combining these findings, we are able to considerably outperform previous methods for self-supervised and semi-supervised learning on ImageNet. A linear classifier trained on self-supervised representations learned by SimCLR achieves 76.5% top-1 accuracy, which is a 7% relative improvement over previous state-of-the-art, matching the performance of a supervised ResNet-50. When fine-tuned on only 1% of the labels, we achieve 85.8% top-5 accuracy, outperforming AlexNet with 100X fewer labels.
Comments: ICML'2020. Code and pretrained models at this https URL
Subjects: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
Cite as: arXiv:2002.05709 [cs.LG]
(or arXiv:2002.05709v3 [cs.LG] for this version)
Submission history
From: Ting Chen [view email]
[v1] Thu, 13 Feb 2020 18:50:45 UTC (5,093 KB)
[v2] Mon, 30 Mar 2020 15:32:51 UTC (5,047 KB)
[v3] Wed, 1 Jul 2020 00:09:08 UTC (5,829 KB)
Reversible Encrypytion and Information ConcealmentIJERA Editor
Recently, a lot of attention is paid to reversible data hiding (RDH) in encrypted pictures, since it maintains the wonderful property that the initial image cover will be losslessly recovered when embedded data is extracted, whereas protects the image content that is need to be kept confidential. Other techniques used antecedently are to embed data by reversibly vacating area from the pictures, that area unit been encryted, may cause some errors on information extraction or image restoration. In this paper, we propose a unique methodology by reserving room before secret writing (i.e reserving room before encryption) with a conventional RDH algorithmic rule, and thus it becomes straightforward for hider to reversibly embed data in the encrypted image. The projected methodology is able to implement real reversibility, that is, information extraction and image recovery area unit free of any error. This methodology embedds larger payloads for constant image quality than the antecedently used techniques, like for PSNR= 40db.
IMAGE COMPRESSION AND DECOMPRESSION SYSTEMVishesh Banga
Image compression is the application of Data compression on digital images. In effect, the objective is to reduce redundancy of the image data in order to be able to store or transmit data in an efficient form.
International Journal of Research in Engineering and Science is an open access peer-reviewed international forum for scientists involved in research to publish quality and refereed papers. Papers reporting original research or experimentally proved review work are welcome. Papers for publication are selected through peer review to ensure originality, relevance, and readability.
An Efficient Block Matching Algorithm Using Logical ImageIJERA Editor
Motion estimation, which has been widely used in various image sequence coding schemes, plays a key role in the transmission and storage of video signals at reduced bit rates. There are two classes of motion estimation methods, Block matching algorithms (BMA) and Pel-recursive algorithms (PRA). Due to its implementation simplicity, block matching algorithms have been widely adopted by various video coding standards such as CCITT H.261, ITU-T H.263, and MPEG. In BMA, the current image frame is partitioned into fixed-size rectangular blocks. The motion vector for each block is estimated by finding the best matching block of pixels within the search window in the previous frame according to matching criteria. The goal of this work is to find a fast method for motion estimation and motion segmentation using proposed model. Recent day Communication between ends is facilitated by the development in the area of wired and wireless networks. And it is a challenge to transmit large data file over limited bandwidth channel. Block matching algorithms are very useful in achieving the efficient and acceptable compression. Block matching algorithm defines the total computation cost and effective bit budget. To efficiently obtain motion estimation different approaches can be followed but above constraints should be kept in mind. This paper presents a novel method using three step and diamond algorithms with modified search pattern based on logical image for the block based motion estimation. It has been found that, the improved PSNR value obtained from proposed algorithm shows a better computation time (faster) as compared to original Three step Search (3SS/TSS ) method .The experimental results based on the number of video sequences were presented to demonstrate the advantages of proposed motion estimation technique.
MULTIPLE RECONSTRUCTION COMPRESSION FRAMEWORK BASED ON PNG IMAGEijcsity
It is shown that neural networks (NNs) achieve excellent performances in image compression and reconstruction. However, there are still many shortcomings in the practical application, which eventually lead to the loss of neural network image processing ability. Based on this, a joint framework based on neural network and scale compression is proposed in this paper. The framework first encodes the incoming PNG image information, and then the image is converted into binary input decoder to reconstruct the intermediate state image, next, we import the intermediate state image into the zooming compressor and repressurize it, and reconstruct the final image. From the experimental results, this method can better process the digital image and suppress the reverse expansion problem, and the compression effect can be improved by 4 to 10 times as much as that of using RNN alone, showing better ability in the application. In this paper, the method is transmitted over a digital image, the effect is far better than the existing compression method alone, the Human visual system cannot feel the change of the effect.
Emblematical image based pattern recognition paradigm using Multi-Layer Perce...iosrjce
The abstract Likewise human brain machine can be signifying diverse pattern sculpt that is
proficiently identify an image based object like optical character, hand character image, fingerprint and
something like this. To present the model of image based pattern recognition perspective by a machine, different
stages are associated like image acquiring from the digitizing image sources, preprocessing image to remove
unwanted data by the normalizing and filtering, extract the feature to represent the data as lower dimension
space and at last return the decision using Multi-Layer Perceptron neural network that is feed feature vector
from got the feature extraction process of a given input image. Performance observation complexity is discussed
rest of the description of pattern recognition model. Our goal of this paper is to introduced symbolical image
based pattern recognition model using Multi-Layer Perceptron learning algorithm in the field of artificial
neural network (like as human-like-brain) with best possible way of utilizing available processes and learning
knowledge in a way that performance can be same as human.
A systematic image compression in the combination of linear vector quantisati...eSAT 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
Data Steganography for Optical Color Image CryptosystemsCSCJournals
In this paper, an optical color image cryptosystem with a data hiding scheme is proposed. In the proposed optical cryptosystem, a confidential color image is embedded into the host image of the same size. Then the stego-image is encrypted by using the double random phase encoding algorithm. The seeds to generate random phase data are hidden in the encrypted stego-image by a content-dependent and low distortion data embedding technique. The confidential image and secret data delivery is accomplished by hiding the image into the host image and embedding the data into the encrypted stego-image. Experimental results show that the proposed data steganographic cryptosystem provides large data hiding capacity and high reconstructed image quality.
High Speed Data Exchange Algorithm in Telemedicine with Wavelet based on 4D M...Dr. Amarjeet Singh
Existing Medical imaging techniques such as fMRI, positron emission tomography (PET), dynamic 3D ultrasound and dynamic computerized tomography yield large amounts of four-dimensional sets. 4D medical data sets are the series of volumetric images netted in time, large in size and demand a great of assets for storage and transmission. Here, in this paper, we present a method wherein 3D image is taken and Discrete Wavelet Transform(DWT) and Dual-Tree Complex Wavelet Transform(DTCWT) techniques are applied separately on it and the image is split into sub-bands. The encoding and decoding are done using 3D-SPIHT, at different bit per pixels(bpp). The reconstructed image is synthesized using Inverse DWT technique. The quality of the compressed image has been evaluated using some factors such as Mean Square Error(MSE) and Peak-Signal to Noise Ratio (PSNR).
ON THE IMAGE QUALITY AND ENCODING TIMES OF LSB, MSB AND COMBINED LSB-MSBijcsit
The Least Significant Bit (LSB) algorithm and the Most Significant Bit (MSB) algorithm are stenography algorithms with each one having its demerits. This work therefore proposed a Hybrid approach and compared its efficiency with LSB and MSB algorithms. The Least Significant Bit (LSB) and Most
Significant Bit (MSB) techniques were combined in the proposed algorithm. Two bits (the least significant bit and the most significant bit) of the cover images were replaced with a secret message. Comparisons were made based on Mean-Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR) and the encoding time between the proposed algorithm, LSB and MSB after embedding in digital images. The combined
technique produced a stego-image with minimal distortion in image quality than MSB technique independent of the nature of data that was hidden. However, LSB algorithm produced the best stego-image quality. Large cover images however made the combined algorithm’s quality better improved. The combined algorithm had lesser time of image and text encoding. Therefore, a trade-off exists between the encoding time and the quality of stego-image as demonstrated in this work.
Image Encryption Based on Pixel Permutation and Text Based Pixel Substitutionijsrd.com
Digital image Encryption techniques play a very important role to prevent image from unauthorized access. There are many types of methods available that can do Image Encryption, and the majority of them are scrambling algorithms based on pixel shuffling, which cannot change the histogram of an image. Hence, their security performances are not good. The encryption method that combines the pixel exchanging and gray level changing can handles reach a good chaotic effect. In this paper we focus on an image encryption technique based on pixel wise shuffling with the help of skew tent map and text based pixel substitution. The PSNR, NPCR and CC obtained by our technique shows that the proposed technique gives better result than the existing techniques.
A simple framework for contrastive learning of visual representationsDevansh16
Link: https://machine-learning-made-simple.medium.com/learnings-from-simclr-a-framework-contrastive-learning-for-visual-representations-6c145a5d8e99
If you'd like to discuss something, text me on LinkedIn, IG, or Twitter. To support me, please use my referral link to Robinhood. It's completely free, and we both get a free stock. Not using it is literally losing out on free money.
Check out my other articles on Medium. : https://rb.gy/zn1aiu
My YouTube: https://rb.gy/88iwdd
Reach out to me on LinkedIn. Let's connect: https://rb.gy/m5ok2y
My Instagram: https://rb.gy/gmvuy9
My Twitter: https://twitter.com/Machine01776819
My Substack: https://devanshacc.substack.com/
Live conversations at twitch here: https://rb.gy/zlhk9y
Get a free stock on Robinhood: https://join.robinhood.com/fnud75
This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank. In order to understand what enables the contrastive prediction tasks to learn useful representations, we systematically study the major components of our framework. We show that (1) composition of data augmentations plays a critical role in defining effective predictive tasks, (2) introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and (3) contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning. By combining these findings, we are able to considerably outperform previous methods for self-supervised and semi-supervised learning on ImageNet. A linear classifier trained on self-supervised representations learned by SimCLR achieves 76.5% top-1 accuracy, which is a 7% relative improvement over previous state-of-the-art, matching the performance of a supervised ResNet-50. When fine-tuned on only 1% of the labels, we achieve 85.8% top-5 accuracy, outperforming AlexNet with 100X fewer labels.
Comments: ICML'2020. Code and pretrained models at this https URL
Subjects: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
Cite as: arXiv:2002.05709 [cs.LG]
(or arXiv:2002.05709v3 [cs.LG] for this version)
Submission history
From: Ting Chen [view email]
[v1] Thu, 13 Feb 2020 18:50:45 UTC (5,093 KB)
[v2] Mon, 30 Mar 2020 15:32:51 UTC (5,047 KB)
[v3] Wed, 1 Jul 2020 00:09:08 UTC (5,829 KB)
Reversible Encrypytion and Information ConcealmentIJERA Editor
Recently, a lot of attention is paid to reversible data hiding (RDH) in encrypted pictures, since it maintains the wonderful property that the initial image cover will be losslessly recovered when embedded data is extracted, whereas protects the image content that is need to be kept confidential. Other techniques used antecedently are to embed data by reversibly vacating area from the pictures, that area unit been encryted, may cause some errors on information extraction or image restoration. In this paper, we propose a unique methodology by reserving room before secret writing (i.e reserving room before encryption) with a conventional RDH algorithmic rule, and thus it becomes straightforward for hider to reversibly embed data in the encrypted image. The projected methodology is able to implement real reversibility, that is, information extraction and image recovery area unit free of any error. This methodology embedds larger payloads for constant image quality than the antecedently used techniques, like for PSNR= 40db.
IMAGE COMPRESSION AND DECOMPRESSION SYSTEMVishesh Banga
Image compression is the application of Data compression on digital images. In effect, the objective is to reduce redundancy of the image data in order to be able to store or transmit data in an efficient form.
International Journal of Research in Engineering and Science is an open access peer-reviewed international forum for scientists involved in research to publish quality and refereed papers. Papers reporting original research or experimentally proved review work are welcome. Papers for publication are selected through peer review to ensure originality, relevance, and readability.
An Efficient Block Matching Algorithm Using Logical ImageIJERA Editor
Motion estimation, which has been widely used in various image sequence coding schemes, plays a key role in the transmission and storage of video signals at reduced bit rates. There are two classes of motion estimation methods, Block matching algorithms (BMA) and Pel-recursive algorithms (PRA). Due to its implementation simplicity, block matching algorithms have been widely adopted by various video coding standards such as CCITT H.261, ITU-T H.263, and MPEG. In BMA, the current image frame is partitioned into fixed-size rectangular blocks. The motion vector for each block is estimated by finding the best matching block of pixels within the search window in the previous frame according to matching criteria. The goal of this work is to find a fast method for motion estimation and motion segmentation using proposed model. Recent day Communication between ends is facilitated by the development in the area of wired and wireless networks. And it is a challenge to transmit large data file over limited bandwidth channel. Block matching algorithms are very useful in achieving the efficient and acceptable compression. Block matching algorithm defines the total computation cost and effective bit budget. To efficiently obtain motion estimation different approaches can be followed but above constraints should be kept in mind. This paper presents a novel method using three step and diamond algorithms with modified search pattern based on logical image for the block based motion estimation. It has been found that, the improved PSNR value obtained from proposed algorithm shows a better computation time (faster) as compared to original Three step Search (3SS/TSS ) method .The experimental results based on the number of video sequences were presented to demonstrate the advantages of proposed motion estimation technique.
MULTIPLE RECONSTRUCTION COMPRESSION FRAMEWORK BASED ON PNG IMAGEijcsity
It is shown that neural networks (NNs) achieve excellent performances in image compression and reconstruction. However, there are still many shortcomings in the practical application, which eventually lead to the loss of neural network image processing ability. Based on this, a joint framework based on neural network and scale compression is proposed in this paper. The framework first encodes the incoming PNG image information, and then the image is converted into binary input decoder to reconstruct the intermediate state image, next, we import the intermediate state image into the zooming compressor and repressurize it, and reconstruct the final image. From the experimental results, this method can better process the digital image and suppress the reverse expansion problem, and the compression effect can be improved by 4 to 10 times as much as that of using RNN alone, showing better ability in the application. In this paper, the method is transmitted over a digital image, the effect is far better than the existing compression method alone, the Human visual system cannot feel the change of the effect.
Emblematical image based pattern recognition paradigm using Multi-Layer Perce...iosrjce
The abstract Likewise human brain machine can be signifying diverse pattern sculpt that is
proficiently identify an image based object like optical character, hand character image, fingerprint and
something like this. To present the model of image based pattern recognition perspective by a machine, different
stages are associated like image acquiring from the digitizing image sources, preprocessing image to remove
unwanted data by the normalizing and filtering, extract the feature to represent the data as lower dimension
space and at last return the decision using Multi-Layer Perceptron neural network that is feed feature vector
from got the feature extraction process of a given input image. Performance observation complexity is discussed
rest of the description of pattern recognition model. Our goal of this paper is to introduced symbolical image
based pattern recognition model using Multi-Layer Perceptron learning algorithm in the field of artificial
neural network (like as human-like-brain) with best possible way of utilizing available processes and learning
knowledge in a way that performance can be same as human.
A systematic image compression in the combination of linear vector quantisati...eSAT 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
Data Steganography for Optical Color Image CryptosystemsCSCJournals
In this paper, an optical color image cryptosystem with a data hiding scheme is proposed. In the proposed optical cryptosystem, a confidential color image is embedded into the host image of the same size. Then the stego-image is encrypted by using the double random phase encoding algorithm. The seeds to generate random phase data are hidden in the encrypted stego-image by a content-dependent and low distortion data embedding technique. The confidential image and secret data delivery is accomplished by hiding the image into the host image and embedding the data into the encrypted stego-image. Experimental results show that the proposed data steganographic cryptosystem provides large data hiding capacity and high reconstructed image quality.
Intelligent Fault Identification System for Transmission Lines Using Artifici...IOSR Journals
Transmission and distribution lines are vital links between generating units and consumers. They are
exposed to atmosphere, hence chances of occurrence of fault in transmission line is very high, which has to be
immediately taken care of in order to minimize damage caused by it. This paper focuses on detecting the faults
on electric power transmission lines using artificial neural networks. A feed forward neural network is
employed, which is trained with back propagation algorithm. Analysis on neural networks with varying number
of hidden layers and neurons per hidden layer has been provided to validate the choice of the neural networks
in each step. The developed neural network is capable of detecting single line to ground and double line to
ground for all the three phases. Simulation is done using MATLAB Simulink to demonstrate that artificial
neural network based method are efficient in detecting faults on transmission lines and achieve satisfactory
performances. A 300km, 25kv transmission line is used to validate the proposed fault detection system.
Hardware implementation of neural network is done on TMS320C6713.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Comparison Between Levenberg-Marquardt And Scaled Conjugate Gradient Training...CSCJournals
The Internet paved way for information sharing all over the world decades ago and its popularity for distribution of data has spread like a wildfire ever since. Data in the form of images, sounds, animations and videos is gaining users’ preference in comparison to plain text all across the globe. Despite unprecedented progress in the fields of data storage, computing speed and data transmission speed, the demands of available data and its size (due to the increase in both, quality and quantity) continue to overpower the supply of resources. One of the reasons for this may be how the uncompressed data is compressed in order to send it across the network. This paper compares the two most widely used training algorithms for multilayer perceptron (MLP) image compression – the Levenberg-Marquardt algorithm and the Scaled Conjugate Gradient algorithm. We test the performance of the two training algorithms by compressing the standard test image (Lena or Lenna) in terms of accuracy and speed. Based on our results, we conclude that both algorithms were comparable in terms of speed and accuracy. However, the Levenberg- Marquardt algorithm has shown slightly better performance in terms of accuracy (as found in the average training accuracy and mean squared error), whereas the Scaled Conjugate Gradient algorithm faired better in terms of speed (as found in the average training iteration) on a simple MLP structure (2 hidden layers).
Image Processing Compression and Reconstruction by Using New Approach Artific...CSCJournals
In this paper a neural network based image compression method is presented. Neural networks offer the potential for providing a novel solution to the problem of data compression by its ability to generate an internal data representation. This network, which is an application of back propagation network, accepts a large amount of image data, compresses it for storage or transmission, and subsequently restores it when desired. A new approach for reducing training time by reconstructing representative vectors has also been proposed. Performance of the network has been evaluated using some standard real world images. It is shown that the development architecture and training algorithm provide high compression ratio and low distortion while maintaining the ability to generalize and is very robust as well.
Scene recognition using Convolutional Neural NetworkDhirajGidde
Scene recognition is one of the hallmark tasks of computer vision, allowing definition of a context for object recognition. Whereas the tremendous recent progress in object recognition tasks is due to the availability of large datasets like ImageNet and the rise of Convolutional Neural Networks (CNNs) for learning high-level features, performance at scene recognition has not attained the same level of success.
Image De-Noising Using Deep Neural Networkaciijournal
Deep neural network as a part of deep learning algorithm is a state-of-the-art approach to find higher level representations of input data which has been introduced to many practical and challenging learning problems successfully. The primary goal of deep learning is to use large data to help solving a given task on machine learning. We propose an methodology for image de-noising project defined by this model and conduct training a large image database to get the experimental output. The result shows the robustness and efficient our our algorithm.
IMAGE DE-NOISING USING DEEP NEURAL NETWORKaciijournal
Deep neural network as a part of deep learning algorithm is a state-of-the-art approach to find higher level representations of input data which has been introduced to many practical and challenging learning problems successfully. The primary goal of deep learning is to use large data to help solving a given task
on machine learning. We propose an methodology for image de-noising project defined by this model and conduct training a large image database to get the experimental output. The result shows the robustness and efficient our our algorithm.
We trained a large, deep convolutional neural network to classify the 1.2 million
high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different
classes. On the test data, we achieved top-1 and top-5 error rates of 37.5%
and 17.0% which is considerably better than the previous state-of-the-art. The
neural network, which has 60 million parameters and 650,000 neurons, consists
of five convolutional layers, some of which are followed by max-pooling layers,
and three fully-connected layers with a final 1000-way softmax. To make training
faster, we used non-saturating neurons and a very efficient GPU implementation
of the convolution operation. To reduce overfitting in the fully-connected
layers we employed a recently-developed regularization method called “dropout”
that proved to be very effective. We also entered a variant of this model in the
ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%,
compared to 26.2% achieved by the second-best entry
Image De-Noising Using Deep Neural Networkaciijournal
Deep neural network as a part of deep learning algorithm is a state-of-the-art approach to find higher level
representations of input data which has been introduced to many practical and challenging learning
problems successfully. The primary goal of deep learning is to use large data to help solving a given task
on machine learning. We propose an methodology for image de-noising project defined by this model and
conduct training a large image database to get the experimental output. The result shows the robustness
and efficient our our algorithm.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
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- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
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https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
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All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
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See how to accelerate model training and optimize model performance with active learning
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Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
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FIDO Alliance Osaka Seminar: Passkeys at Amazon.pdf
M017427985
1. IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 4, Ver. II (July – Aug. 2015), PP 79-85
www.iosrjournals.org
DOI: 10.9790/0661-17427985 www.iosrjournals.org 79 | Page
Image Upscaling and Fuzzy ARTMAP Neural Network
Gagandeep Singh1
, Gulshan Goyal2
1
(Computer Science Department, Chandigarh University, India)
2
(Computer Science Department, Chandigarh University, India)
Abstract: In Image upscaling, a higher resolution image is processed from an available low resolution image.
There are numerous scenarios and applications where image upscaling is required and performed. The paper
proposes a simple image scaling algorithm and implements it with a fuzzy variant of the supervised ART neural
network, ARTMAP. The method has been compared with nearest neighbor interpolation, bilinear interpolation
and bicubic interpolation, both objectively and subjectively. The present paper is an attempt at understanding
the Adaptive Resonance Theory networks while proposing a simple linear upscaling method.
Keywords: ARTMAP, Artificial neural network, bilinear, nearest neighbor, Upscaling
I. Introduction
Digital image processing is a field of computing that deals with processing and operations on digitally
acquired images. Image processing entails various operations like enhancement, restoration, segmentation,
feature extraction etc. Image upscaling is one of the various applications of the wide field of digital image
processing. In upscaling, we aim to produce a high resolution replica of an image from the original low
resolution image as shown in Fig. 1. The goal of image interpolation is to produce acceptable images at
different resolutions from a single low-resolution image. The actual resolution of an image is defined as the
number of pixels, but the effective resolution is a much harder quantity to define as it depends on subjective
human judgment and perception[1]. It so happens, that we quite often come across situations when we have to
deal with an image which is big enough spatially to view or observe the subject(s) comfortably or effectively. In
such situations we need to obtain a bigger image, a higher resolution image, which is easier to work with. This is
where image upscaling or up-sampling comes in. It takes a small image as input, performs some operations and
gives a bigger replica. It should be noted however, that up-sampling the image will not add any more detail to
the original image. A simple definition image is still simple definition, upscaling will only increase the spatial
resolution; not enhance the definition or detailing to the image than it already has.
Fig 1: Original image to Upscaled image
The image upscaling problem is also referred to as ‘single-image super-resolution’ problem.
1.1 Need for Image Upscaling
Scaling of images from lower resolution to a higher resolution is needed because of the following reasons:
1) It’s easier to analyze and study higher resolution images.
2) Available sensors have limitation in respect to maximum resolution[2] So we need upscaling to overcome
some of the inherent resolution limitations of low-cost imaging sensors [3]
3) To produce images of high perceptual quality and produce visually appealing results
4) To keep the text and graphics as original as possible, while avoiding noise and artifacts in the image [4]
5) To preserve the nature and texture of image while enlarging it.
6) To allow for better utilization of the growing capability of High Resolution displays (e.g., HD LCDs). [3]
1.2 Applications of Image Upscaling
Image upscaling (and more generally image interpolation) methods are implemented in a variety of
computer tools like printers, digital TV, media players, image processing packages, graphics renderers and so
on.[4]. Some application areas of image upscaling/ single image super resolution are as follows:
2. Image Upscaling And Fuzzy ARTMAP Neural Network
DOI: 10.9790/0661-17427985 www.iosrjournals.org 80 | Page
1) Printing: To scale and print images according to canvas size while maintaining picture quality.
2) Video playback: Televisions use real time up scaling algorithms to display simple definition content on
high-resolution displays.
3) Mobile devices: To scale graphics and videos onto varying display sizes for viewing and playback.
4) Image processing packages: Image processing software use scaling for viewing and resizing the image.
[4]
5) Satellite imaging: Nowadays, satellite images are used in many applications such as geosciences studies,
astronomy, and geographical information systems. [5]
6) Computer Graphics: Computers perform screen image scaling which includes web pages, text, graphics,
game scenes etc. [6]
1.3 Methods for image upscaling
There are many ways of upscaling via image interpolation. Some methods are adaptive and some are
non-adaptive.
Linear or Non-adaptive methods: The non-adaptive methods are quicker. The non-adaptive approach applies
same operations on all the pixels of the image, without taking into account any of its characteristics.
Non-linear or Adaptive methods: The performance of conventional approaches is only acceptable for small
upscaling factors[7].The adaptive methods rely on the assumption that, in natural images, frequency
components are not all equally probable [8]. The adaptive methods perform a little better but have the downside
to being too complex and higher processing times. The adaptive methods apply operations depending upon
certain characteristics and features like presence of curves, edges, the image quality etc.
Adaptive algorithm basically uses some of the image features and improves interpolation result. Adaptive
algorithm works based on different intensity present and local structure of image. Many adaptive interpolation
algorithms have been proposed which enhance edges in images. Local gradient information can also be used to
enhance non adaptive interpolation algorithm. Normal problem with interpolation technique is blurring and
blocking artifacts. This can be solved using directional interpolation. For example, Edge directed interpolation,
NEDI (New Edge Directed Interpolation), SAI (Soft decision adaptive Interpolation).
II. Artificial Neural Networks
An artificial neural network is an information processing paradigm that is inspired by and tries to
mimic the biological nervous system. In a biological nervous system, the decision making process is facilitated
by neurons and neural pathways that fire upon stimuli and transfer the desired signal to the brain. In the same
way, the artificial neural network tries to mimic the decision making process and information transfer abilities of
the biological nervous system. In an artificial neural network the nodes represent the neurons of a nervous
system, the interconnections between neurons are imitated by the use of weights and biases over the inter-
connections among the neurons.
The artificial neural network or ANN can be a single layer network or a multiple layered network. A
single layered neural network does not have hidden layers in between the input and output layers. Usually multi-
layered neural networks are implemented and they have more than one hidden layers in between the input and
output layers. Each hidden layer is connected to others via weights. The higher the number of layers in a
network, the more complex operations can be performed by the ANN.
Fig 2: A simple neural network
N1 N2 N3
1.1 INTRODUCTION TO DIGITAL IM
Digital image processing is a field of computing
on digitally acquired images. Image processing en
restoration, segmentation, feature extraction etc. I
Hidden layer Output layer
YX
w1 w2
Input layer
3. Image Upscaling And Fuzzy ARTMAP Neural Network
DOI: 10.9790/0661-17427985 www.iosrjournals.org 81 | Page
The Fig. 3 shows an example of a simple neural network. Where X is the input to the network. N1, N2,
N3 are the input, hidden and output layers of the neural network respectively. The intermediate connections
between the layers are represented by weighted connections w1 and w2.
2.1 Artmap Neural Network
ARTMAP neural network is a supervised form of ART neural network. ART neural network has an
inherently un-supervised approach to learning, which may be unsuitable for certain situations and applications.
The supervised nature of an ARTMAP network makes it more reliable and better at learning. ARTMAP is
among the family of neural networks that are capable of fast supervised, incremental learning and prediction. A
number of variants of the network are available, like distributed ARTMAP, fuzzy ARTMAP, default ARTMAP,
probabilistic ARTMAP etc.
The fuzzy ARTMAP network has integrated the fuzzy ART which enables it to process both analog
and binary-valued input patterns. This neural network is simple and has been designed with the ability to
perform supervised learning. In supervised learning mode, the sequential learning process grows the number of
recognition categories according to a problem’s complexity. [11]
Fig 3: An ARTMAP structure [9]
The Fig.3 above shows a basic ARTMAP structure, where A is the input to the network. F1, F2 are its
layers. And the ART subsystem is connected to the Map Field via weights Wab.
The Map Field determines
whether the prediction of the network is equal to what was desired by the network.
Because this neural network architecture has a small number of parameters and requires no problem-
specific system crafting or choice of initial weight values, it is also easy to use. In an ARTMAP network, an
epoch is defined as one cycle of training on an entire set of input examples [10]. The fuzzy ARTMAP algorithm
is discussed in the Fig.4 below.
Fig. 4: The fuzzy ARTMAP algorithm
4. Image Upscaling And Fuzzy ARTMAP Neural Network
DOI: 10.9790/0661-17427985 www.iosrjournals.org 82 | Page
III. Proposed Method
The proposed method aims to upscaleimages by gradually increasing or decreasing the intensity values
between the existing pixels in order to interpolate new pixel values. The method avoids interpolation by taking
average of the intensity values. Fig.5 discusses the algorithm.
Fig. 5: The proposed algorithm
IV. Results And Discussion
The work has been implemented using MATLAB version R2014a. The system had the following configuration
1. Processor - AMD ® A8-6410APU
2. RAM - 8.00 GB (6.95 GB usable)
3. Operating System – Windows 8.1, 64-bit.
This section compares the outputs of the three existing methods and the proposed method both
objectively and subjectively. The subjective analysis compares the output images of the algorithms. Objective
analysis compares the performance evaluation parameters obtained after the implementation of the respective
methods.
Results of the existing methods and the proposed method with ARTMAP are evaluated on the basis of the
following performance evaluation parameters:
1. Peak Signal to Noise Ratio (PSNR)
2. Mean Square Error (MSE)
3. Structural similarity index (ssim)
Fig. 6: Some examples of outputs of existing methods and ARTMAP implementation
6. Image Upscaling And Fuzzy ARTMAP Neural Network
DOI: 10.9790/0661-17427985 www.iosrjournals.org 84 | Page
Fig. 8: Graph comparing the structural similarity of outputs
Fig. 9: Graph comparing the Mean square error values of outputs
From left to right, the bars represent nearest neighbor, bilinear, bicubic and implemented ARTMAP methods
respectively.
Fig 10: Comparison of RMSE values of ARTMAP and existing methods
V. Conclusion And Future Scope
The proposed algorithm is still in its early stages and has proved to be better than nearest neighbor interpolation
and bilinear interpolation in all manners. It’s also subjectively good when compared with images produced by
bicubic interpolation.
The proposed method was able to improve on the problem of rough, jagged edges in images and too much
blurring of images. The proposed upscaling algorithm still has some room for improvement in terms of
performance parameters. Also the neural network can be upgraded in the future to work with grayscale and
colored images as well, making it more useful.
7. Image Upscaling And Fuzzy ARTMAP Neural Network
DOI: 10.9790/0661-17427985 www.iosrjournals.org 85 | Page
Based on the obtained values and results, we make the following conclusions. Advantages of training ARTMAP
with proposed algorithm:
Better PSNR values than nearest neighbor
Better MSE values than Nearest neighbor
Structural similarity and RMSE are also satisfactory
Even though the proposed method scores lower than bicubic interpolation during objective evaluation,
subjectively its output quality is quite good. It also deals with the too much blurring and ringing artifacts in the
images to a large extent.
References
[1]. Gonzalez, Rafael C. Digital image processing. Pearson Education India, 2009.
[2]. Purkait, P., and Chanda B., "Super resolution image reconstruction through Bregman iteration using morphologic
regularization." Image Processing, IEEE Transactions on 21.9 (2012): pp 4029-4039.
[3]. Yang, Jianchao, et al. "Coupled dictionary training for image super-resolution."Image Processing, IEEE Transactions on 21.8
(2012):pp 3467-3478.
[4]. Zhang, Haichao, Yanning Zhang, and Thomas S. Huang. "Efficient sparse representation based image super resolution via dual
dictionary learning."Multimedia and Expo (ICME), 2011 IEEE International Conference on. IEEE, 2011.
[5]. Gunturk, B. K., et al. "Demosaicking: color filter array interpolation." IEEE Signal Processing Magazine 22 (2005):pp 44-54.
[6]. Han, Dianyuan. "Comparison of Commonly Used Image Interpolation Methods."Proceedings of the 2nd International Conference
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[7]. Yang, Jianchao, et al. "Coupled dictionary training for image super-resolution."Image Processing, IEEE Transactions on 21.8
(2012): pp 3467-3478.
[8]. Giachetti, Andrea, and Nicola Asuni. "Real-time artifact-free image upscaling."Image Processing, IEEE Transactions on 20.10
(2011): pp 2760-2768.
[9]. Granger, Eric, et al. "Supervised learning of fuzzy artmap neural networks through particle swarm optimization." Journal of Pattern
Recognition Research2.1 (2007): pp 27-
[10]. Connolly, Jean-François, Eric Granger, and Robert Sabourin. "An adaptive classification system for video-based face
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[11]. Taherian, Arash, and Mahdi Aliyari Sh. "Noise Resistant Identification of Human Iris Patterns Using Fuzzy ARTMAP Neural
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