CS image denoising reconstruction method based on hyperspectral total variation

Abstract

The invention provides a CS image denoising reconstruction method based on hyperspectral total variation. The CS image denoising reconstruction method comprises the following steps: initializing a reconstructed image, an iterative index value and a noisy observation value; iteratively updating the obtained reconstructed image by using the noisy observation value to obtain an estimated value; respectively inputting the estimated values into a CS reconstruction model based on the l1-norm and the HTV to obtain an intermediate reconstruction image; performing sparse representation on the intermediate reconstructed image by using Starlet transform to obtain a Starlet coefficient; performing denoising filtering on the Starlet coefficient by using the new threshold operator and the improved BayeShrink threshold to obtain a curvelet coefficient; performing Starlet inverse transformation on the curvelet coefficient to obtain a reconstructed image; and judging whether an iteration stopping condition is met or not, and carrying out loop iteration. According to the method, while most noise information in the high-noise image is removed, details, textures and other feature information in the image can be effectively protected, the method is easy to implement and high in robustness, and the denoising reconstruction problem of the high-noise image is effectively solved.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to a CS image denoising and reconstruction method based on hyperspectral total variation, which is used for denoising and reconstruction of high-noise images, and realizes high-efficiency high-resolution images under high-noise conditions. denoising capability. Background technique [0002] With the continuous progress of modern science and technology, CMOS / CCD sensor technology has also been developed rapidly, and the impact on us is mainly reflected in two aspects: (1) image quality is getting higher and higher; (2) image resolution Higher and higher. Although high-resolution images can bring us high visual enjoyment, they also bring new challenges to the field of image processing. When shooting high-resolution images at night, due to the influence of the night environment, the image data usually obtained contains a lot of noise information; in addition, when sh...

AI Technical Summary

Problems solved by technology

[0006] Aiming at the technical problem of poor performance of denoising and reconstruction of high-noise images in existing CS reconstruction methods, the present invention proposes a CS image denoising and reconstruction method based on hyperspectral total variation, which uses the established CS reconstruction model to iteratively update the reconstructed image , use the Starlet transform to sparsely represent the high-noise image to obtain the Starlet coefficients, use the designed new threshold operator to filter the Starlet data of the obtained image in each iteration process, and effectively protect the details in the image while removing the noise Feature information can effectively realize high-quality reconstruction of high-resolution images under high-noise conditions

Images