Kavli Affiliate: Li Xin Li
| First 5 Authors: Bingchen Li, Xin Li, Yiting Lu, Ruoyu Feng, Mengxi Guo
| Summary:
Blind Compressed Image Restoration (CIR) has garnered significant attention
due to its practical applications. It aims to mitigate compression artifacts
caused by unknown quality factors, particularly with JPEG codecs. Existing
works on blind CIR often seek assistance from a quality factor prediction
network to facilitate their network to restore compressed images. However, the
predicted numerical quality factor lacks spatial information, preventing
network adaptability toward image contents. Recent studies in
prompt-learning-based image restoration have showcased the potential of prompts
to generalize across varied degradation types and degrees. This motivated us to
design a prompt-learning-based compressed image restoration network, dubbed
PromptCIR, which can effectively restore images from various compress levels.
Specifically, PromptCIR exploits prompts to encode compression information
implicitly, where prompts directly interact with soft weights generated from
image features, thus providing dynamic content-aware and distortion-aware
guidance for the restoration process. The light-weight prompts enable our
method to adapt to different compression levels, while introducing minimal
parameter overhead. Overall, PromptCIR leverages the powerful transformer-based
backbone with the dynamic prompt module to proficiently handle blind CIR tasks,
winning first place in the NTIRE 2024 challenge of blind compressed image
enhancement track. Extensive experiments have validated the effectiveness of
our proposed PromptCIR. The code is available at
https://github.com/lbc12345/PromptCIR-NTIRE24.
| Search Query: ArXiv Query: search_query=au:”Li Xin Li”&id_list=&start=0&max_results=3