Weather removal with a lightweight quaternion Chebyshev neural network

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.

Review and evaluation of recent advancements in image dehazing techniques for vision improvement and visualization

“…The methods provided a novel quaternion encoder-decoder structure with multilevel feature fusion and quaternion instance normalization. Quaternion operations 73 enable modeling spatial relations involving rotation for real-time computer vision and deep learning applications. The advantages of QCNNs 74 provide a desirable option for improving efficiency on different computational image processing and visualization tasks, especially when combined with a newly developed effective quaternion convolutions technique 75,76 built around matrix decompositions.…”