High Resolution Remote Sensing Image Denoising Algorithm Based on Sparse Representation and Adaptive Dictionary Learning

Aiming at the denoising algorithm of high resolution remote sensing images, in this paper, we propose a novel method based on sparse representation and adaptive dictionary learning. The proposed algorithm uses the strong correlation between the bands of high resolution remote sensing images, which combines the non local self similarity of the image with the local sparsity to improve the denoising performance. By means of sparse representation of image noise, we extract texture information from image noise so as to improve the quality of image denoising. A learning based super-resolution algorithm learns a dictionary through a set of training examples, and combines the missing high-frequency information from the low resolution image, and finally obtains the corresponding high-resolution image. The traditional denoising algorithm still has noise residue after noise removal, and the image denoising effect is not obvious when the noise is large. Experimental results show that the peak signal-to-noise ratio of the proposed method is higher than the existing similar algorithms, and it can better preserve the details and texture information of the image, and improve the visual effect.