A Novel Adaptive Group Sparse Representation Model Based on Infrared Image Denoising for Remote Sensing Application

Abstract

Infrared (IR) Image preprocessing is aimed at image denoising and enhancement to help with small target detection. According to the sparse representation theory, the IR original image is low rank, and the coefficient shows a sparse character. The low rank and sparse model could distinguish between the original image and noise. The IR images lack texture and details. In IR images, the small target is hard to recognize. Traditional denoising methods based on nuclear norm minimization (NNM) treat all eigenvalues equally, which blurs the concrete details. They are unable to achieve a good denoising performance. Deep learning methods necessitate a large number of train images, which are difficult to obtain in IR image denoising. It is difficult to perform well under high noise in IR image denoising. Tracking and detection would not be possible without a proper denoising method. This article fuses the weighted nuclear norm minimization (WNNM) with an adaptive similar patch, searching based on the group sparse representation for infrared images. We adaptively selected similar structural blocks based on certain computational criteria, and we used the K-nearest neighbor (KNN) cluster to constitute more similar groups, which is helpful in recovering the complex background with high Gaussian noise. Then, we shrank all eigenvalues with different weights in the WNNM model to solve the optimization problem. Our method could recover more detailed information in the images. The algorithm not only obtains good denoising results in common image denoising but also achieves good performance in infrared image denoising. The target in IR images attains a high signal for the clutter in IR detection systems for remote sensing. Under common data sets and real infrared images, it has a good noise suppression effect with a high peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM), with higher noise and a much more complex background.