Adaptive Noise Detector and Partition Filter for Image Restoration

The random-value impulse noise (RVIN) detection approach in image denoising, which is dependent on manually defined detection thresholds or local window information, does not have strong generalization performance and cannot successfully cope with damaged pictures with high noise levels. The fusion of the K-means clustering approach in the noise detection stage is reviewed in this research, and the internal relationship between the flat region and the detail area of the damaged picture is thoroughly explored to suggest an unique two-stage method for gray image denoising. Based on the concept of pixel clustering and grouping, all pixels in the damaged picture are separated into various groups based on gray distance similarity features, and the best detection threshold of each group is solved to identify the noise. In the noise reduction step, a partition decision filter based on the gray value characteristics of pixels in the flat and detail areas is given. For the noise pixels in flat and detail areas, local consensus index (LCI) weighted filter and edge direction filter are designed respectively to recover the pixels damaged by the RVIN. The experimental results show that the accuracy of the proposed noise detection method is more than 90%, and is superior to most mainstream methods. At the same time, the proposed filtering method not only has good noise reduction and generalization performance for natural images and medical images with medium and high noise but also is superior to other advanced filtering technologies in visual effect and objective quality evaluation.