Restoration of Bitstream-Corrupted Images: A Mamba-based Thumbnail-guided Network

This paper investigates the real-world JPEG image restoration problem with bit errors on the compressed bitstream. To mimic the effect of bit errors encountered in real images, we automatically inject various bit errors to generate damaged images, thereby simulating the bitstream-corrupted JPEG images in real situations. The image restoration problem is proposed to recover these images caused by bit errors that conventional decoders cannot perfectly decode. Typically, when a bit stream containing bit errors is decoded by the robust decoder, the resulting image exhibits two distinct characteristics: color casts and block shifts. To solve those problems, we propose a Mamba-based thumbnail-guided network to address the impact of color casts and block shifts on the image. The proposed framework is structurally divided into three blocks. Firstly, we use a feature aggregation (FA) block to reassemble the information from the corrupted image and the thumbnail image into a more acceptable input format for the subsequent networks, allowing for self-adjustment of the input format. Then, we design a point-to-point restoration (PPR) block as a decoder to parse the features of inputs and thumbnails to generate coarse images. Finally, with the guidance of coarse images, a pyramid fusion (PF) block is used to generate the refined images. Extensive experimental results demonstrate our model outperforms state-of-the-art methods. Ablation studies and comparisons with super-resolution methods illustrate the effectiveness of our approach. The code will be available at https://github.com/HU1qy/MambaThumbnail.