Prompt to Restore, Restore to Prompt: Cyclic Prompting for Universal Adverse Weather Removal

A recent development in the field of universal adverse weather removal is the introduction of a method called CyclicPrompt. This approach leverages cyclic prompts inspired by pre-trained vision-language models such as CLIP to enhance image restoration processes. By utilizing these innovative cyclic prompts, CyclicPrompt aims to improve the removal of weather-related distortions from images, facilitating weather-free image restoration. The method has been reported to significantly boost the effectiveness of adverse weather removal techniques. This advancement suggests promising potential for future applications in image processing under challenging weather conditions. The approach builds on the capabilities of existing vision-language models, integrating them into a cyclic prompting framework. Overall, CyclicPrompt represents a notable step forward in addressing the challenges posed by adverse weather in visual data.