Abstract: In order to make better use of image information and enhance the visual effect of images, image denoising has become a hot issue in the field of image processing. To address the image denoising problem, this paper proposes two image denoising algorithms based on weighted low-rank matrix padding on the basis of low-rank matrix padding theory. Firstly, the low-rank matrix is formed by extracting similar patches based on patch matching; secondly, the low-rank matrix with missing terms is formed by using the nature of similar patches; then, the denoising model of patch blocks is constructed by using the weighted kernel parametrization; finally, the weighted low-rank matrix denoising optimization model is solved based on the singular value threshold decomposition and the optimisation of a minimisation singular value threshold, respectively, to obtain the weighted matrix padding based on singular value threshold decomposition.The SVT-MC denoising algorithm and the MMST denoising algorithm based on the optimization-minimization singular value threshold are obtained. The experimental results show that both the SVT-MC denoising algorithm and the MMST denoising algorithm have good denoising effects on different levels of mixed noise.