Visibility in Bad Weather from a Single Image

Slides:



Advertisements
Similar presentations
Statistical Inference
Advertisements

Another example of interference that is often observed is interference from a thin film. Light incident on the surface of a thin film is reflected from.
Packet Video Error Concealment With Auto Regressive Model Yongbing Zhang, Xinguang Xiang, Debin Zhao, Siwe Ma, Student Member, IEEE, and Wen Gao, Fellow,
Context-based object-class recognition and retrieval by generalized correlograms by J. Amores, N. Sebe and P. Radeva Discussion led by Qi An Duke University.
Atmospheric Optics - I. Recap Condensation above the Earth surface produces clouds. Clouds are divided into 4 main groups: ♦ High ♦ Middle ♦ Low ♦ Clouds.
Color Seamlessness in Multi-Projector Displays Using Constrained Gamut Morphing IEEE Visualization, 2009 Behzad Sajadi Maxim Lazarov Aditi Majumder M.
A Practical Analytic Model for Daylight
Approaches for Retinex and Their Relations Yu Du March 14, 2002.
Based on slides created by Edward Angel
1 Angel: Interactive Computer Graphics 5E © Addison-Wesley 2009 Shading I.
Vehicle-Infrastructure-Driver Interactions Research Unit
One-Shot Multi-Set Non-rigid Feature-Spatial Matching
Modeling Pixel Process with Scale Invariant Local Patterns for Background Subtraction in Complex Scenes (CVPR’10) Shengcai Liao, Guoying Zhao, Vili Kellokumpu,
Shree Nayar and Srinivasa Narasimhan Computer Science Columbia University ICCV Conference Korfu, Greece, September 1999 Sponsors: NSF Vision in Bad Weather.
Light Mixture Estimation for Spatially Varying White Balance
Apparent Greyscale: A Simple and Fast Conversion to Perceptually Accurate Images and Video Kaleigh SmithPierre-Edouard Landes Joelle Thollot Karol Myszkowski.
Introduction to Image Quality Assessment
Color Calculator Xiaoyan Song Feb.21,2003.
1 CSCE 641: Computer Graphics Lighting Jinxiang Chai.
Chromatic Framework for Vision in Bad Weather Srinivasa G. Narasimhan and Shree K. Nayar Computer Science Department Columbia University IEEE CVPR Conference.
Trichromacy Helmholtz thought three separate images went forward, R, G, B. Wrong because retinal processing combines them in opponent channels. Hering.
Lecture outline Support vector machines. Support Vector Machines Find a linear hyperplane (decision boundary) that will separate the data.
Removing Weather Effects from Monochrome Images Srinivasa Narasimhan and Shree Nayar Computer Science Department Columbia University IEEE CVPR Conference.
Chapter 6 Color Image Processing Chapter 6 Color Image Processing.
1 Angel: Interactive Computer Graphics 4E © Addison-Wesley 2005 Shading I Ed Angel Professor of Computer Science, Electrical and Computer Engineering,
CS 480/680 Computer Graphics Shading I Dr. Frederick C Harris, Jr.
Recursive Bilateral Filtering F Reference Yang, Qingxiong. "Recursive bilateral filtering." ECCV Deriche, Rachid. "Recursively implementating.
Shading (introduction to rendering). Rendering  We know how to specify the geometry but how is the color calculated.
COMPUTER GRAPHICS CS 482 – FALL 2014 AUGUST 27, 2014 FIXED-FUNCTION 3D GRAPHICS MESH SPECIFICATION LIGHTING SPECIFICATION REFLECTION SHADING HIERARCHICAL.
Mutual Information-based Stereo Matching Combined with SIFT Descriptor in Log-chromaticity Color Space Yong Seok Heo, Kyoung Mu Lee, and Sang Uk Lee.
Shedding Light on the Weather
Multiple Scattering in Vision and Graphics Lecture #21 Thanks to Henrik Wann Jensen.
Overview of Haze Removal Methods Matteo Pedone Machine Vision Group, University of Oulu, Finland.
Object Stereo- Joint Stereo Matching and Object Segmentation Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on Michael Bleyer Vienna.
Verifying the “Consistency” of Shading Patterns and 3-D Structures Pawan Sinha & Edward Adelson.
Colour changes in a natural scene due to the interaction between the light and the atmosphere Raúl Luzón González Colour Imaging Laboratory.
Tools of Astronomy.
Basic Illumination BRANCH :- ELECTRICAL (C11) Gohil Amit M. ( ) Joshi Jay P. ( ) Barot Mahesh P. ( ) Pandya Bhrugu I.
Computer Graphics: Programming, Problem Solving, and Visual Communication Steve Cunningham California State University Stanislaus and Grinnell College.
1 Introduction to Computer Graphics with WebGL Ed Angel Professor Emeritus of Computer Science Founding Director, Arts, Research, Technology and Science.
Digital Image Processing In The Name Of God Digital Image Processing Lecture6: Color Image Processing M. Ghelich Oghli By: M. Ghelich Oghli
UNIT 1: WAVES Lesson 6 : Reflection of Waves. Reflection - when a wave bounces off of an object  Reflection is what allows you to see an object that.
Lightness filtering in color images with respect to the gamut School of Electrical Engineering and Computer Science Kyungpook National Univ. Fourteenth.
1 CSCE 441: Computer Graphics Lighting Jinxiang Chai.
Improving Image Matting using Comprehensive Sampling Sets CVPR2013 Oral.
Images for reflection. Que
1 CSCE 441: Computer Graphics Lighting Jinxiang Chai.
Use of Java for Demonstration of Color Science Concepts Presentation of an MS Project, submitted to The Faculty of the Computer Science Department, in.
1 CSCE 441: Computer Graphics Lighting Jinxiang Chai.
Space Tools Standard Compare the purposes of the tools & the technology that scientists use to study space.
Using Clouds Shadows to Infer Scene Structure and Camera Calibration
3D Rendering 2016, Fall.
Why is the sky blue?.
Electromagnetic Waves
Lecture 13 review Explain how distance vector algorithm works.
Single Image Haze Removal Using Dark Channel Prior
Light lesson 1 key concepts
RAY OPTICS - II Refraction through a Prism
Light Ch 27.1 – 27.3 & 28.1 – 28.2 & 28.8 – 28.9.
RAY OPTICS - II Refraction through a Prism
Find the velocity of a particle with the given position function
Primary rainbow Secondary rainbow Rainbows:
Refraction Optical Phenomena.
Color Image Processing
Illumination and Shading
Light.
Last Time Liang-Barsky Details Weiler-Atherton clipping algorithm
Color image noise removal algorithm utilizing hybrid vector filtering
Refraction Optical Phenomena.
Presentation transcript:

Visibility in Bad Weather from a Single Image Robby T. Tan Imperial College London CVPR. 2008

Outline Introduce Model Algorithm Result Future work and conclusion

x is the 2D spatial location. L∞ is the atmospheric light. ρ is the reflectance of an object in the image. β is the atmospheric attenuation coefficient. d is the distance between an object in the image and the observer.

Define image chromaticity Assume distant (d = ∞) , since e^-βd = 0 , light chromaticity Assume no effect of scattering particles e^-βd = 1 , object chromaticity

By utilizing the light chromaticity (α) we can transform the color of the atmospheric light of the input image into white color Color vectors：

Maximizing contrast

Airlight Smoothness Constraint Data term Smooth term

Algorithm

Future work and conclusion 失真情形

Feedback: Privacy Policy Feedback
Do Not Sell My Personal Information
About project: SlidePlayer Terms of Service

© 2025 SlidePlayer.com. Inc. All rights reserved.