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

Understand

Images used in real-world applications such as image or video retrieval, outdoor surveillance, and autonomous driving suffer from poor weather conditions.

• When designing robust computer vision systems, removing adverse weather such as haze, rain, and snow is a significant problem.
• Recently, deep-learning methods offered a solution for a single type of degradation.
• Current state-of-the-art universal methods struggle with combinations of degradations, such as haze and rain-streak.