CS iteration threshold image denoising reconstruction method based on spatial adaptive total variation

Abstract

The invention provides a CS iteration threshold image denoising reconstruction method based on spatial adaptive total variation. The method comprises the following steps: initializing a reconstructed image and an initial noisy observation value; iteratively updating the obtained reconstructed image to obtain an estimated value; contourlet transformation is carried out based on an optimized threshold soft threshold operator to obtain a denoising estimation value contourlet sparse coefficient; inputting the obtained denoising estimation value contourlet sparse coefficient into a reconstruction model based on spatial adaptive total variation CS to obtain a reconstructed image contourlet coefficient; filtering by using a Wiener filtering operator based on a contourlet to obtain a contourlet coefficient of the reconstructed image; and carrying out contourlet inverse transformation on the reconstructed image contourlet coefficient to obtain a reconstructed image. According to the method, sparse transformation is carried out by adopting contourlet transformation based on an optimized soft threshold operator, so that not only can image data and noise information be effectively separated, but also the noise information hidden in each layer of the image can be effectively removed to obtain a high-quality image sparse coefficient, and the denoising reconstruction capability is improved.

Description

technical field [0001] The present invention relates to the technical field of image denoising, in particular to a CS iterative threshold image denoising and reconstruction method based on spatial adaptive total variation, which is suitable for denoising and reconstruction of high-resolution and high-noise images, especially involving high-noise conditions at night Denoising reconstruction of high-resolution images under . Background technique [0002] At present, the use of computer vision technology to improve the all-weather real-time monitoring and effective personnel management of important national security areas and urban sensitive public places has become a research topic that countries around the world attach great importance to. Especially in the environment with poor lighting conditions at night, due to the working characteristics of the camera sensor, the image information obtained in the night environment usually contains a lot of noise information, which affect...

AI Technical Summary

Problems solved by technology

[0006] Aiming at the technical problem that the existing CS denoising reconstruction algorithm does not make full use of sparse transformation information and affects the image denoising and reconstruction quality, the present invention proposes a CS iterative threshold image based on Spatial Adaptive Total Variation (SATV) The denoising and reconstruction method improves the denoising ability of sparse transformation, improves the denoising and reconstruction quality of high-noise high-resolution images, and can effectively remove high-quality reconstruction of high-resolution images under high-noise conditions

Images