Multi-scale Haze Removal via Residual Network
Abstract
The scattering of climatic particles significantly alters images captured under unfavorable weather situations. Although many traditional methods have efficiently committed to annihilating haze, they pose some limitation due to their hand-crafted features, e.g., dark channel, maximum contrast, and colour disparity. This paper presents an end-to-end algorithm to restore a hazy image using a residual-based deep CNN straightforwardly. The proposed algorithm is non-subject to the climatic dispersing model, yet it learns the mapping relationship within the hazy input image and their corresponding transmission map. The network architecture constitutes a convolution kernel and multi-scale fusion layers in extracting relevant features in predicting a holistic propagation map. Ultimately, we obtain the residual image and circumvent the loss of information using a residual network. Comprehensive empirical results demonstrate that the proposed technique outperforms multiple conventional algorithms.
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Published In
March 2021
132 pages
ISBN:9781450388917
DOI:10.1145/3459212
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Published: 20 July 2021
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- THE SCIENCE AND TECHNOLOGY PROJECT OF SICHUAN 2020
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IVSP 2021
IVSP 2021: 2021 3rd International Conference on Image, Video and Signal Processing
March 19 - 21, 2021
Singapore, Singapore
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