Open AccessEnd to end system for hazy image classification and reconstruction based on mean channel prior using deep learning network
Author(s) -
Satrasupalli Sivaji,
Daniel Ebenezer,
Guntur Sitaramanjaneya Reddy,
Shehanaz Shaik
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2020.0923
Subject(s) - computer science , artificial intelligence , ground truth , block (permutation group theory) , channel (broadcasting) , computer vision , transmission (telecommunications) , deep learning , noise (video) , artificial neural network , similarity (geometry) , image (mathematics) , end to end principle , pattern recognition (psychology) , telecommunications , mathematics , geometry
Outdoor images are having several applications including autonomous vehicles, geo‐mapping, and surveillance. It is a common phenomenon that the images captured outdoor are prone to noise, which arises due to natural and manmade extreme atmospheric conditions such as haze, fog, and smog. Importantly in autonomous vehicle navigation, it is very important to recover the ground truth image to get the better decision by the system. Estimation of the transmission map and air‐light is very crucial in recovering the ground truth image. In this study, the authors proposed a new method to estimate the transmission map based on a mean channel prior (MCP), which represents the depth map to estimate the transmission map. The authors proposed a deep neural network to identify the hazy image for the further dehazing process. In this study, the authors presented, two novel contributions, first an MCP‐based image dehazing and second, a deep neural network‐based identification of hazy images as a pre‐processing block in the proposed end to end system. The proposed deep learning network using the TensorFlow platform provided validation accuracy of 93.4% for hazy image classification. Finally, the proposed MCP‐based dehazing network showed better performance in terms of peak‐signal‐to‐noise ratio, structural similarity index, and computational time than that of existing methods.