Research On Algorithm Of Color Image Restoration Based On Total Variation

Image restoration is an important research content in digital image processing,and color image restoration is an important branch of image restoration.A color image in a computer,unlike a two-dimensional grayscale image,is an image with a three-channel structure.Therefore,there is a certain difference from the grayscale image during the processing.Color image restoration problems include color image denoising and color image deblurring.This paper combines these two problems and studies the effective algorithm for denoising and deblurring of color images.In the image restoration problem,because the total variational model can well maintain the edge information of the image,it has been widely used.However,the non-differentiability of the canonical term of the total variational model makes the numerical optimization difficult.In order to solve this problem,we first use the alternating direction multiplier algorithm(ADMM)to solve the model,convert the original minimization model into a dual model,and use the augmented Lagrangian multiplier technique to split the model and solve them separately.Then the first-order original dual algorithm combining the partition matrix inversion is used to solve the model.This algorithm is an improvement over the first-order original dual algorithm.The algorithm first uses the conjugate transformation and the Lagrangian multiplier to transform the model.The minimization problem is transformed into a minimax problem.Then,the block matrix inversion algorithm is used to solve the problem that it is difficult to inverse the large-dimensional complex matrix and affect the iterative speed.The solution to the above minimum-maximum problem is solved.Finally,the effectiveness of the two algorithms is verified by numerical experiments.The experimental results of these two algorithms are compared.It is verified that the first-order original dual algorithm combined with partition matrix inversion is better.