Remote-Sensing Image Denoising With Multi-Sourced Information

Abstract

In this paper, a new multi-source-information-based method for remote-sensing image denoising is proposed. Different from the conventional denoising algorithm, the proposed method employs the features of similar reference images from the different band, different sensors, or from multi-temporal images when denoising a target image. The noise-free reference images as a prior is introduced into the denoising object function. The prior's information about the reference image is explored in two aspects: dictionary learning and edge-feature prediction. For dictionary learning, we improve the basis training process by incremental singular value decomposition. For edge-feature prediction, we construct the relationship between gradients of the target image and the reference image by linear ridge regression. The new denoising object function employs both the sparsity of the coefficient and the edge similarity between the target image and the reference image. We also present the optimization scheme for the proposed denoising model. Some typical cases based on different feature relations between a target image and a reference image are comprehensively discussed. Reasonably utilizing the similarity between the target image and the reference image, the proposed algorithm smooths out more noise and conserves more detail at the same time. Better performance of the proposed method is confirmed when compared with other state-of-the-art reference-based denoising methods.