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https://github.com/muonray/image_denoising_with_rof_algorithm

The total variation denoising method, proposed by Rudin, Osher and Fatermi, circa 1992, is a PDE-based algorithm for edge-preserving noise removal. The ROF denoising algorithm is based on the partial differential of total variation of an image such that by finding a good appoximation of the local minima of the PD of the total variation value we can find that the total noise in the image will also be in a local minima. Unlike a conventional low-pass filter, TV denoising is defined in terms of an optimization problem. In effect the output of the TV denoising 'filter' is obtained by minimizing a particular bounded cost function. Total variation itself is a measure of the complexity of an image with respect to its spatial variation, usually an integral of the greyscale gradient across the rectangular image domain.
https://github.com/muonray/image_denoising_with_rof_algorithm

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The total variation denoising method, proposed by Rudin, Osher and Fatermi, circa 1992, is a PDE-based algorithm for edge-preserving noise removal. The ROF denoising algorithm is based on the partial differential of total variation of an image such that by finding a good appoximation of the local minima of the PD of the total variation value we can find that the total noise in the image will also be in a local minima. Unlike a conventional low-pass filter, TV denoising is defined in terms of an optimization problem. In effect the output of the TV denoising 'filter' is obtained by minimizing a particular bounded cost function. Total variation itself is a measure of the complexity of an image with respect to its spatial variation, usually an integral of the greyscale gradient across the rectangular image domain.

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