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.

翻译：盲压缩图像恢复（CIR）因其实际应用而备受关注。其目标在于缓解由未知品质因子（特别是JPEG编解码器）引起的压缩伪影。现有盲CIR工作常借助品质因子预测网络来辅助其恢复网络处理压缩图像。然而，预测的数值型品质因子缺乏空间信息，导致网络难以自适应图像内容。近期基于提示学习的图像恢复研究展现了提示在泛化不同退化类型与程度上的潜力。这启发我们设计了名为PromptCIR的基于提示学习的压缩图像恢复网络，该网络能有效恢复不同压缩等级下的图像。具体而言，PromptCIR利用提示隐式编码压缩信息，提示直接与图像特征生成的软权重交互，从而为恢复过程提供动态的内容感知与畸变感知引导。轻量化的提示使得我们的方法能适应不同压缩等级，同时仅引入极少的参数开销。总体而言，PromptCIR借助强大的Transformer骨干网络与动态提示模块，熟练处理盲CIR任务，并在NTIRE 2024盲压缩图像增强赛道中荣获第一名。大量实验验证了我们提出的PromptCIR的有效性。代码开源于https://github.com/lbc12345/PromptCIR-NTIRE24。