  The generalization capability of existing image restoration and enhancement
(IRE) methods is constrained by the limited pre-trained datasets, making it
difficult to handle agnostic inputs such as different degradation levels and
scenarios beyond their design scopes. Moreover, they are not equipped with
interactive mechanisms to consider user preferences or feedback, and their
end-to-end settings cannot provide users with more choices. Faced with the
above-mentioned IRE method's limited performance and insufficient
interactivity, we try to solve it from the engineering and system framework
levels. Specifically, we propose Clarity ChatGPT-a transformative system that
combines the conversational intelligence of ChatGPT with multiple IRE methods.
Clarity ChatGPT can automatically detect image degradation types and select
appropriate IRE methods to restore images, or iteratively generate satisfactory
results based on user feedback. Its innovative features include a CLIP-powered
detector for accurate degradation classification, no-reference image quality
evaluation for performance evaluation, region-specific processing for precise
enhancements, and advanced fusion techniques for optimal restoration results.
Clarity ChatGPT marks a significant advancement in integrating language and
vision, enhancing image-text interactions, and providing a robust,
high-performance IRE solution. Our case studies demonstrate that Clarity
ChatGPT effectively improves the generalization and interaction capabilities in
the IRE, and also fills the gap in the low-level domain of the existing
vision-language model.
