SGDFormer: One-stage Transformer-based Architecture for Cross-Spectral Stereo Image Guided Denoising
<|reference_start|>SGDFormer: One-stage Transformer-based Architecture for Cross-Spectral Stereo Image Guided Denoising: Cross-spectral image guided denoising has shown its great potential in recovering clean images with rich details, such as using the near-infrared image to guide the denoising process of the visible one. To obtain such image pairs, a feasible and economical way is to employ a stereo system, which is widely used on mobile devices. Current works attempt to generate an aligned guidance image to handle the disparity between two images. However, due to occlusion, spectral differences and noise degradation, the aligned guidance image generally exists ghosting and artifacts, leading to an unsatisfactory denoised result. To address this issue, we propose a one-stage transformer-based architecture, named SGDFormer, for cross-spectral Stereo image Guided Denoising. The architecture integrates the correspondence modeling and feature fusion of stereo images into a unified network. Our transformer block contains a noise-robust cross-attention (NRCA) module and a spatially variant feature fusion (SVFF) module. The NRCA module captures the long-range correspondence of two images in a coarse-to-fine manner to alleviate the interference of noise. The SVFF module further enhances salient structures and suppresses harmful artifacts through dynamically selecting useful information. Thanks to the above design, our SGDFormer can restore artifact-free images with fine structures, and achieves state-of-the-art performance on various datasets. Additionally, our SGDFormer can be extended to handle other unaligned cross-model guided restoration tasks such as guided depth super-resolution.<|reference_end|>
@article{zhang2024sgdformer:,
    title={SGDFormer: One-stage Transformer-based Architecture for Cross-Spectral
  Stereo Image Guided Denoising},
    author={Runmin Zhang, Zhu Yu, Zehua Sheng, Jiacheng Ying, Si-Yuan Cao, Shu-Jie
  Chen, Bailin Yang, Junwei Li and Hui-Liang Shen},
    journal={arXiv preprint arXiv:2404.00349},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.00349},
    primaryClass={cs.CV}
}