This document describes a method for exponential contrast restoration of images captured during fog conditions to improve visibility for driving assistance systems. It begins with an introduction to how fog degrades image quality and decreases visibility distance. It then describes Koschmieder's law which models luminance attenuation through fog. The proposed method estimates the atmospheric veil through exponential modeling and uses it to restore contrast. Results show the restored images have higher clarity and more visible edges than other methods. The technique allows real-time enhancement of color and grayscale images captured in homogeneous or heterogeneous fog.
Digital image processing Tool presentationdikshabehl5392
The development of this image processing software will help editing process to be done effectively. It requires less space on hard disk; emphasizing only on the crucial image processing functions and the executable program will take less space.
Digital image processing Tool presentationdikshabehl5392
The development of this image processing software will help editing process to be done effectively. It requires less space on hard disk; emphasizing only on the crucial image processing functions and the executable program will take less space.
its very useful for students.
Sharpening process in spatial domain
Direct Manipulation of image Pixels.
The objective of Sharpening is to highlight transitions in intensity
The image blurring is accomplished by pixel averaging in a neighborhood.
Since averaging is analogous to integration.
Prepared by
M. Sahaya Pretha
Department of Computer Science and Engineering,
MS University, Tirunelveli Dist, Tamilnadu.
COM2304: Intensity Transformation and Spatial Filtering – I (Intensity Transf...Hemantha Kulathilake
At the end of this lesson, you should be able to;
describe spatial domain of the digital image.
recognize the image enhancement techniques.
describe and apply the concept of intensity transformation.
express histograms and histogram processing.
describe image noise.
characterize the types of Noise.
describe concept of image restoration.
COM2304: Intensity Transformation and Spatial Filtering – II Spatial Filterin...Hemantha Kulathilake
At the end of this lecture, you should be able to;
describe the fundamentals of spatial filtering.
generating spatial filter masks.
identify smoothing via linear filters and non linear filters.
apply smoothing techniques for problem solving.
Moving Vehicle Detection from a Video, CCTV Footage etc. by Image Processing. The algorithm and the steps to be followed for detection is described in the presentation.
Neural Scene Representation & Rendering: Introduction to Novel View SynthesisVincent Sitzmann
An overview over the neural scene representation and rendering framework and an introduction to novel view synthesis approaches. Slides made for the Eurographics, CVPR, and SIGGRAPH courses on neural rendering, connected to the state-of-the-art report on Neural Rendering at Eurographics 2020.
Feel free to re-use the slides! I just ask that you keep some form of attribution, either at the beginning of your presentation, or in the slide footer.
its very useful for students.
Sharpening process in spatial domain
Direct Manipulation of image Pixels.
The objective of Sharpening is to highlight transitions in intensity
The image blurring is accomplished by pixel averaging in a neighborhood.
Since averaging is analogous to integration.
Prepared by
M. Sahaya Pretha
Department of Computer Science and Engineering,
MS University, Tirunelveli Dist, Tamilnadu.
COM2304: Intensity Transformation and Spatial Filtering – I (Intensity Transf...Hemantha Kulathilake
At the end of this lesson, you should be able to;
describe spatial domain of the digital image.
recognize the image enhancement techniques.
describe and apply the concept of intensity transformation.
express histograms and histogram processing.
describe image noise.
characterize the types of Noise.
describe concept of image restoration.
COM2304: Intensity Transformation and Spatial Filtering – II Spatial Filterin...Hemantha Kulathilake
At the end of this lecture, you should be able to;
describe the fundamentals of spatial filtering.
generating spatial filter masks.
identify smoothing via linear filters and non linear filters.
apply smoothing techniques for problem solving.
Moving Vehicle Detection from a Video, CCTV Footage etc. by Image Processing. The algorithm and the steps to be followed for detection is described in the presentation.
Neural Scene Representation & Rendering: Introduction to Novel View SynthesisVincent Sitzmann
An overview over the neural scene representation and rendering framework and an introduction to novel view synthesis approaches. Slides made for the Eurographics, CVPR, and SIGGRAPH courses on neural rendering, connected to the state-of-the-art report on Neural Rendering at Eurographics 2020.
Feel free to re-use the slides! I just ask that you keep some form of attribution, either at the beginning of your presentation, or in the slide footer.
An Efficient Visibility Enhancement Algorithm for Road Scenes Captured by Int...madhuricts
The visibility of images of outdoor road scenes will
generally become degraded when captured during inclement
weather conditions. Drivers often turn on the headlights of their
vehicles and streetlights are often activated, resulting in localized
light sources in images capturing road scenes in these conditions.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Single Image Fog Removal Based on Fusion Strategy csandit
Images of outdoor scenes are degraded by absorption and scattering by the suspended particles and water droplets in the atmosphere. The light coming from a scene towards the camera is attenuated by fog and is blended with the airlight which adds more whiteness into the scene. Fog removal is highly desired in computer vision applications. Removing fog from images can
significantly increase the visibility of the scene and is more visually pleasing. In this paper, we propose a method that can handle both homogeneous and heterogeneous fog which has been tested on several types of synthetic and real images. We formulate the restoration problem
based on fusion strategy that combines two derived images from a single foggy image. One of
the images is derived using contrast based method while the other is derived using statistical
based approach. These derived images are then weighted by a specific weight map to restore
the image. We have performed a qualitative and quantitative evaluation on 60 images. We use
the mean square error and peak signal-to-noise ratio as the performance metrics to compare
our technique with the state-of-the-art algorithms. The proposed technique is simple and shows
comparable or even slightly better results with the state-of-the-art algorithms used for
defogging a single image.
Many applications such as robot navigation, defense, medical and remote sensing perform
various processing tasks, which can be performed more easily when all objects in different images of the
same scene are combined into a single fused image. In this paper, we propose a fast and effective
method for image fusion. The proposed method derives the intensity based variations that is large and
small scale, from the source images. In this approach, guided filtering is employed for this extraction.
Gaussian and Laplacian pyramidal approach is then used to fuse the different layers obtained.
Experimental results demonstrate that the proposed method can obtain better performance for fusion of
all sets of images. The results clearly indicate the feasibility of the proposed approach.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
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Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
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About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
3. INTRODUCTION
• Different natural phenomena can reduce the quality of images and diminish
the visibility.
• Visibility distance is decreased because of the absorption and scattering of
light by the atmospheric particles.
• Images of outdoor scenes, captured during fog conditions, are drastically
degraded.
3
4. • Due to the presence of fog, the visibility distance decreases exponentially.
• Negative effects of fog on the quality of the image are the loss of contrast and
the alteration of the natural colours in the captured image.
• The scattering effect of the transmitted light causes additional lightness in parts
of the image. This effect is called air-light or atmospheric veil.
• Several algorithms for restoring the contrast of foggy images.
4
5. • These methods can be categorized in two groups: model and non-model based
enhancement techniques.
• Non-model based methods perform image enhancement relying only on the
information obtained from the image.
• Unfortunately, these methods do not maintain colour fidelity and are not
suitable for real time computer vision.
• Model based contrast restoration techniques can be further divided in two
categories : with given depth and unknown depth.
5
6. • When the depth is supposed to be known, this information can be used to
restore the original contrast of the image.
• The depth is inferred by using the altitude, tilt and position of the camera.
• It through the manual approximation of the sky area and vanishing point in the
captured image.
• Contrast restoration techniques can be used in a wide range of applications like
in photography fields.
6
7. KOSCHMIEDER’S LAW
• A relationship between the attenuation of an object’s luminance L at distance
d and the luminance L0 close to the object
• L∞ is the atmospheric luminance and β is the extinction coefficient
• This equation states that the luminance of an object seen through fog is
attenuated with an exponential factor e−βd
7
……………(1)
8. 8
• The atmospheric veil obtained from daylight scattered by fog between the object
and the observer is expressed by L∞ (1 − e−βd )
• The response function of a camera can be applied to the Koschmieder’s
equation for to model the mapping from scene luminance to image intensity.
• Thus, the intensity perceived in the image is the result of a function f applied to
the sum of the air light A and the direct transmission T
……………(2)
9. 9
The eqn represents a linear mapping and assuming that
So we get
where R represents the pixel intensity of the image without fog and A∞ is the
intensity of the sky in fog conditions.
……….(3)
……….(4)
……….(5)
11. 11
• Our approach is to estimate the atmospheric veil and then to use it in
order to compute the original fog free image.
A. Algorithm Overview
• By using a single image we are not able to compute the depth in the scene
hence we introduce the notion of atmospheric veil (V ) as
……….(6)
……….(7)
……….(8)
……….(9)
12. 12
• The intensity of the sky (A∞) is considered to be equal to 255 or it can be
inferred as the maximum intensity in the image.
B. Obtaining the Atmospheric Veil
• Our visibility enhancement method must be able to work with both gray
scale and color images.
• For to compute the atmospheric veil for color images, use input as gray
level image W that consists in the minimum of each color channel (R,G,B).
13. 13
• This image is called the dark channel prior of a color image.
• The obtained veil V provides the amount of white that must be subtracted
from each color channel
• In order to infer the atmospheric veil we must first examine some
properties that it must have.
• First, the photometric constraint must be introduced , V must be higher or
equal to zero and V must be lower than W
……….(10)
14. 14
0 ≤ V ≤ W
• Another property of the atmospheric veil is that V must be smooth
function
• No black pixel constraint states that the local standard deviation of the
enhanced pixels around a given pixel position must be lower than its local
average.
• We can infer that the veil is smaller or equal to the difference between the
average and the standard deviation of the input image W
V ≤ Average(W) ≤ std(W)
……….(11)
……….(12)
15. 15
• A median filter with a variable size (k) will be applied on the W image
instead of using the classical average
M = mediank (W)
• Use the classical standard deviation computed in each pixel of the image
on the obtained W image
• Fog is more present in the top part of the image and tends to disappear in
the bottom part of the image.
• So the median filter along columns is the one that yields the best results.
……….(13)
16. 16
• Also consider that only a percentage p will be used to calculate the value for
the atmospheric veil in each pixel.
• This percentage is used to control the strength of the restoration process.
• The usual values for p are set from 85% to 99%.
• So the equation for computing the atmospheric veil becomes
……….(14)
17. 17
C. Exponential Inference of the Atmospheric Veil
• The atmospheric veil computed with this method is over compensated in the
bottom part of the image, thus resulting in very dark restored images.
• So this method is not suited for traffic scenes, because it does not model the
link between the veil and the distance to the objects in the scene.
• There is a need for a smooth exponential function.
• For this reason we will model an exponential filter on the atmospheric veil
such that this exponential function decreases inversely with the distance.
18. 18
• Our approach is to model the atmospheric veil with an exponential filter in
order to recover this link.
• We treat the whole atmospheric veil as an exponential function.
19. 19
• The final formula for computing the atmospheric veil is
Vfinal = V . G
where G is an exponential function with values between 0 and 1.
• For modelling the function G, start from two exponential functions from the
partition of unity
• We can call them squared and modulus partition of unity functions.
• Let fso : [−a, a] → [0, 1] (squared) and fmo : [−a, a] → [0, 1] (modulus) having
the following form.
……….(15)
20. 20
• For using the functions fso and fmo in image processing, we must modify
them such that they are defined in the image domain and take values in the
[0,1] interval.
• For this reason the variable x will denote the image lines.
• Hence we obtain fs : [0,H − 1] → [0, 1] and fm : [0,H − 1] → [0, 1], two new
exponential functions with the following form:
……….(17)
……….(16)
22. 22
• A translation of our exponential function (along the x axis) has to be applied
by using a linear isomorphism A : [vh,Max] → [0,H − 1]
A(x) = ax + b
having the following properties
……….(20)
……….(21)
23. 23
By solving the system of equations presented in equation (21) the final
exponential function becomes
where c = (H − 1)/(Max − vh)
……….(22)
28. 28
• The rate of new visible edges in the enhanced images. This metric can be
used in order to assess the quality of the restoration process.
• The percent of new visible edges in the enhanced image e is represented by
the following formula: nr : total number of edge points in the
enhanced images
no : number of edge points in the original
foggy image.
• The percentage of pixels that become completely black or completely white
after restoration
……….(23)
……….(24)
29. ADVANTAGES
• Single-image is needed for this enhancement algorithm.
• Able to obtain superior reconstructions of the original fog-free image when
compared with traditional methods.
• It has the ability to adapt the model in accordance to the density of the fog.
• Suitable for contrast restoration in both homogeneous and heterogeneous
fog conditions.
• It can perform contrast restoration in real time for both color and grayscale
images.
29
30. CONCLUSION
• A new image enhancement method was proposed by taking into account the
exponential decay present in foggy images.
• Computes the restored image by estimating the atmospheric veil.
• The clarity of the reconstructed scene is higher than the one using other
median type filters especially in the regions of the image with many details
• Methods using the squared and modulus translated exponential functions
achieve the best results for image enhancement in fog conditions.
30
31. 31
REFERENCE
• V. Cavallo, M. Colomb, and J. Doré, “Distance perception of vehicle rear lights in fog.” Human Factors,
vol. 43, no. 3, pp. 442–451, 2001.
• M. Negru and S. Nedevschi, “Image based fog detection and visibility estimation for driving assistance
systems,” in Proc. IEEE Int. Conf. ICCP,Sep. 2013, pp. 163–168.
• J. Oakley and H. Bu, “Correction of simple contrast loss in color images,” IEEE Trans. Image Process.,
vol. 16, no. 2, pp. 511–522, Feb. 2007.
• M. Pavlic, H. Belzner, G. Rigoll, and S. Ilic, “Image based fog detection in vehicles,” in Proc. IEEE IV,
Jun. 2012, pp. 1132–1137.
• K. Mori et al., “Recognition of foggy conditions by in-vehicle camera and millimeter wave radar,” in
Proc. IEEE Intell. Veh. Symp., Jun. 2007, pp. 87–92.