Anisotropic multi-directional total variation image denoising method and device

Abstract

The invention provides an anisotropic multi-directional total variation image denoising method and device. The steps of the method include establishing a multi-directional anisotropic total variation model, and performing regularization operations on the original image to be denoised (multi-directional differential operations such as horizontal difference, vertical difference, and diagonal line); The established anisotropic multi-directional total variation model is solved to obtain the denoised image. For the first time, the invention adds the full variation difference operation in the diagonal direction to the full variation model to denoise the image, and effectively optimizes the information such as the oblique direction of the image; it can effectively remove the influence of noise, and at the same time effectively maintain the image. The edge feature information provides good image quality for subsequent processing.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to an anisotropic multi-directional total variation image denoising method and device. Background technique [0002] The image is inevitably affected by noise during the acquisition process. The total variation (TV) image denoising method is currently an effective denoising method. This method regards the denoising process of the image as a piecewise constant model, The total variation model and iterative calculation are established to realize the denoising process of the image. However, the total variation image denoising method only optimizes the horizontal and vertical gradient information, and does not take into account the oblique information of the image, which makes the model lack the optimization of the oblique gradient information of the image. [0003] At present, in the existing denoising methods based on total variation, some methods weight the pixels of the im...

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Problems solved by technology

[0003] At present, in the existing denoising methods based on total variation, some methods weight the pixels of the image before performing differential operations; some methods first smooth the total variation norm of the image, and then The total variation norm is iteratively solved, but this method will lose the detailed information of the image; some methods divide the image into local small blocks for local total variation filtering, but the computational complexity of this method is very high. time consuming to run

[0004] In short, the existing image denoising methods based on total variation do not take into account the optimization of the oblique gradient information of the image, and cannot better suppress the noise of the image.

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