Every year lives and properties are lost in road accidents. About one-fourth of these accidents are due to low vision in foggy weather. At present, there is no algorithm that is specifically designed for the removal of fog from videos. Application of a single-image fog removal algorithm over each video frame is a time-consuming and costly affair. It is demonstrated that with the intelligent use of temporal redundancy, fog removal algorithms designed for a single image can be extended to the real-time video application. Results confirm that the presented framework used for the extension of the fog removal algorithms for images to videos can reduce the complexity to a great extent with no loss of perceptual quality. This paves the way for the real-life application of the video fog removal algorithm. In order to remove fog, an efficient fog removal algorithm using anisotropic diffusion is developed. The presented fog removal algorithm uses new dark channel assumption and anisotropic diffusion for the initialization and refinement of the airlight map, respectively. Use of anisotropic diffusion helps to estimate the better airlight map estimation. The said fog removal algorithm requires a single image captured by uncalibrated camera system. The anisotropic diffusion-based fog removal algorithm can be applied in both RGB and HSI color space. This book shows that the use of HSI color space reduces the complexity further. The said fog removal algorithm requires pre- and post-processing steps for the better restoration of the foggy image. These pre- and post-processing steps have either data-driven or constant parameters that avoid the user intervention. Presented fog removal algorithm is independent of the intensity of the fog, thus even in the case of the heavy fog presented algorithm performs well. Qualitative and quantitative results confirm that the presented fog removal algorithm outperformed previous algorithms in terms of perceptual quality, color fidelity and execution time. The work presented in this book can find wide application in entertainment industries, transportation, tracking and consumer electronics.

Autorentext

Sudipta Mukhopadhyay is currently Associate Professor in the Electrical and Electrical Communication Engineering, IIT Kharagpur. He received his B.E. degree from Jadavpur University, Kolkata, in 1988. He received his M.Tech. and Ph.D. degrees from IIT Kanpur in 1991 and 1996 respectively. He has served several companies including TCS, Silicon Automation Systems, GE India Technology Centre and Philips Medical Systems before joining IIT Kharagpur in 2005 as Assistant Professor of Electrical and Electrical Communication Engineering, IIT Kharagpur. In 2013 he become Associate Professor in the same department. He has authored or co-authored more than 70 publications in the field of signal and image processing. He has filed seven patents while working in industry and continued the trend after joining academia. He is a senior member of the Institute of Electrical and Electronics Engineers (IEEE), Member of SPIE and corresponding member of Radiological Society of North America (RSNA). He has done many applied projects sponsored by DIT, Intel and GE Medical Systems IT, USA. He is also founder and director of Perceptivo Imaging Technologies Private Ltd., a company under the guidance of S.T.E.P. IIT Kharagpur. The company specializes in developing innovative software for signal and image processing.Abhishek Tripathi is currently working as Senior Engineer at Uurmi Systems Pvt. Ltd., Hyderabad, India. He received his Ph.D. degree from Indian Institute of Technology Kharagpur, India, in 2012. He received the M.Tech. degree from National Institute of Technology, Kurukshetra, India, in 2008. He received the B.Tech. degree from Uttar Pradesh Technical University, Lucknow, India, in 2006. His research interests include computer vision, image-based rendering, nonlinear image processing, physics-based vision, video post processing, recognition, machine learning and medical imaging.

Inhalt
Acknowledgments.- Introduction.- Analysis of Fog.- Dataset and Performance Metrics.- Important Fog Removal Algorithms.- Single-Image Fog Removal Using an Anisotropic Diffusion.- Video Fog Removal Framework Using an Uncalibrated Single Camera System.- Conclusions and Future Directions.- Bibliography.- Authors' Biographies .