RCST: Residual Context-Sharing Transformer Cascade to Approximate Taylor Expansion for Remote Sensing Image Denoising
Zhenghua Huang, Yang Yang, Haoyong Yu, Qian Li, Yu Shi, Yaozong Zhang, Hao Fang
Abstract
Taylor expansion is a polynomial for approximating a function constructed by the coefficients of its derivatives at a certain point, where it is a challenging research to utilize the powerful learning ability of deep learning (DL) to characterize the polynomial parts for pursuing its approximate solution. In this article, we develop a cascading residual context-sharing Transformer (RCST) to approximate Taylor expansion for remote sensing (RS) image denoising. Our RCST method includes the following key procedures. First, a mapping function about a latent clean RS image patch is built by employing the low-rank characteristic of its neighborhood RS image blocks, and is expanded into a polynomial with Taylor expansion for its approximate solution. Second, the intrinsic recursive relationship of the neighborhood derivatives is analyzed and is mathematically formulated, which provides a theoretical interpretability for the construction of our RCST model. Third, a lightweight residual network (LRNet) is developed to estimate the base layer, while the RCST is shared to calculate the derivative parts. Finally, to transfer as many rich multiscale details from noisy RS images to estimated results as possible, we adopt a down-/upsampling architecture. Specifically, a spatial-Fourier upsampling (SFUS) operator is reported to preserve both local and global information. Quantitatively and qualitatively experimental results validate that our RCST denoising method can achieve competitive performance and is even superior to other SOTA denoising approaches.