The article presents a noise reduction method based on minimizing a multicriteria objective function. The technique makes it possible to perform minimization according to the criteria of the root-mean-square difference of the deviation between adjacent estimates of pixel values (vertical, horizontal) and between the mean-square difference of the input elements and the resulting estimates. The first criterion allows you to reduce the noise component in locally stationary areas of the image, the second to preserve the boundaries of transitions between objects. In the article, the adaptation of the choice of the processing parameter is performed using a trained neural network. The training was carried out on standard test images from widely used databases (Kodak, MS COCO, etc.). Tables comparing the effectiveness of the proposed adaptation algorithm to the previously applied approach are given.
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