CMAWRNet: Multiple Adverse Weather Removal via a Unified Quaternion Neural Architecture.

Area of Science:

• Computer Vision
• Deep Learning
• Image Processing

Background:

• Adverse weather conditions like haze, rain, and snow degrade image quality in real-world applications.
• Existing deep learning methods often fail to address multiple simultaneous weather degradations.
• Robust computer vision systems require effective adverse weather removal techniques.

Purpose of the Study:

• To develop an efficient solution for removing multiple adverse weather conditions from images.
• To introduce a unified quaternion neural architecture for universal weather removal.
• To enhance the performance of downstream computer vision applications.

Main Methods:

• Developed CMAWRNet, a novel quaternion neural architecture.
• Introduced a texture-structure decomposition block and a lightweight encoder-decoder quaternion transformer.
• Incorporated an attentive fusion block with low-light correction and a quaternion similarity loss function.

Main Results:

• CMAWRNet demonstrates superior performance in quantitative and qualitative evaluations on benchmark datasets and real-world images.
• The proposed method effectively handles combined weather artifacts, outperforming state-of-the-art approaches.
• The decomposition approach is applied for the first time to the universal weather removal task.

Conclusions:

• CMAWRNet offers an efficient and effective solution for multiple adverse weather removal.
• The novel architecture and techniques significantly improve image quality and preserve color information.
• The method enhances the performance of downstream applications, such as object detection.