A Novel Multi-Scale Residual Dense Dehazing Network (MSRDNet) for Single Image Dehazing✱
Authors: 
Chippy M Manu, Sreeni K GAuthors Info & Claims
Article No.: 1, Pages 1 - 9
Abstract
Dehazing is a difficult process because of the damage caused by the non-uniform fog and haze distribution in images. To address these issues, a Multi-Scale Residual dense Dehazing Network (MSRDNet) is proposed in this paper. A Contextual feature extraction module (CFM) for extracting multi-scale features and an Adaptive Residual Dense Module (ARDN) are used as sub-modules of MSRDNet. Moreover, all the hierarchical features extracted by each ARDN are fused, which helps to detect hazy maps of varying lengths with multi-scale features. This framework outperforms the state-of-the-art dehazing methods in removing haze while maintaining and restoring image detail in real-world and synthetic images captured under various scenarios.
Supplementary Material
References
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- A Novel Multi-Scale Residual Dense Dehazing Network (MSRDNet) for Single Image Dehazing✱
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Published: 12 May 2023
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ICVGIP'22
ICVGIP'22: Thirteenth Indian Conference on Computer Vision, Graphics and Image Processing
December 8 - 10, 2022
Gandhinagar, India
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