Learnable Representative Coefficient Image Denoiser for Hyperspectral Image

作者：Jiangjun Peng, Hailin Wang, Xiangyong Cao, Qian Zhao, Jing Yao, Hongying Zhang, Deyu Meng · 发表于：IEEE Transactions on Geoscience and Remote Sensing · 年份：2024 · DOI：10.1109/tgrs.2024.3357981 · 被引用次数：28 · 研究领域：Image and Signal Denoising Methods、Advanced Image Fusion Techniques、Remote-Sensing Image Classification

Fully characterizing the spatial-spectral priors of hyperspectral images (HSI) is crucial for HSI denoising tasks. Recently, HSI denoising models based on representative coefficient images (RCIs) under the spectral low-rank decomposition framework have garnered significant attention due to their clever utilization of spatial-spectral information in HSI at a low cost. However, current methods either employ handcrafted classical denoisers or off-the-shelf deep denoisers to denoise RCIs, failing to fully capture the structural information of RCIs. In this paper, we propose a specific optimization framework for learning an RCI denoiser under the low-rank decomposition framework for the first time. Since low-rank decomposition can characterize the global low-rank property of HSI, our RCI denoiser only needs to learn the spatial prior of RCIs. Consequently, our optimization framework is inclined to learn a more powerful RCI denoiser. However, learning an RCI denoiser is not an easy task, primarily due to the lack of paired clean-noisy RCI data. To address this issue, we employ parametric techniques to represent the to-be-restored HSI as a function of RCI denoiser network parameters. In this way, the parameters of the RCI denoiser can thus be updated using noisy-clean HSI pairs. Furthermore, we adopt residual learning and Gaussian whitening techniques to enhance the RCI denoiser’s denoising ability for HSIs with various noise levels and different rank settings. Extensive experiments...