It was proposed to develop a better multiscale learning dictionary picture de-noising technique. The approach improves the adaptive threshold curvilinear transform, which can divide an image into different scale information and be used to build a curvilinear multiscale learning dictionary. The method finished the dictionary and sparse coefficient updates in the picture through circular iterations and then superimposed the matching curvilinear wave domain image blocks and performed the curvilinear inverse transform to generate the denoised image. The test was carried out by adding additive Gaussian noise to a standard grayscale image, and the results revealed that the peak signal-to-noise ratio of the grayscale image de-noising result of this paper’s method was improved by 56.6% on average, and the structural similarity was improved by 0.44 on average, compared to the conventional de-noising algorithm. It was determined that the enhanced approach preserved the picture’s edge and texture information well, that image quality was greatly improved, and that the algorithm’s execution efficiency was superior to that of the conventional de-noising algorithm.
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