Weather removal with a lightweight quaternion Chebyshev neural network

Vladimir Frants; Sos Agaian

doi:10.1117/12.2664858

15 June 2023 Weather removal with a lightweight quaternion Chebyshev neural network

Vladimir Frants, Sos Agaian

Proceedings Volume 12526, Multimodal Image Exploitation and Learning 2023 ; 125260V (2023) https://doi.org/10.1117/12.2664858
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Conference Poster

Abstract

The performance of real-world CV systems used in outdoor surveillance and autonomous vehicles severely suffers from adverse weather conditions. Removing mist, rain streaks, adherent raindrops, and snow is an important processing step in real-world applications. Several solutions based on deep learning were proposed for multiple-type weather removal. Existing methods are prohibitively expensive regarding computational requirements and aren’t suitable for real-time operation. We propose ChebTF–lightweight encoder-decoder architecture based on quaternion neural network principles and a novel polynomial transform block to address this issue. The assessment of synthetic benchmarking datasets and realworld images, both quantitatively and qualitatively, demonstrates the comparable performance of the proposed ChebTF in handling various weather artifacts compared to other leading weather removal methods.

Citation Download Citation

Vladimir Frants and Sos Agaian "Weather removal with a lightweight quaternion Chebyshev neural network", Proc. SPIE 12526, Multimodal Image Exploitation and Learning 2023 , 125260V (15 June 2023); https://doi.org/10.1117/12.2664858

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