2017/11/29 by Nikolay Gapon, Ponomarenko, Mykola, Gapon, Nikolay +4 · 1 citation
Computer Science · Engineering · #Blind Source Separation Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image Processing Techniques and Applications #Image and Signal Denoising Methods
paper · pdf · doi:10.48550/arxiv.1711.10792
openalex publication_date 2017/11/29 · openalex created_date 2022/10/04 · openalex updated_date 2026/07/28
In the paper, a new method of blind estimation of noise variance in a single\nhighly textured image is proposed. An input image is divided into 8x8 blocks\nand discrete cosine transform (DCT) is performed for each block. A part of 64\nDCT coefficients with lowest energy calculated through all blocks is selected\nfor further analysis. For the DCT coefficients, a robust estimate of noise\nvariance is calculated. Corresponding to the obtained estimate, a part of\nblocks having very large values of local variance calculated only for the\nselected DCT coefficients are excluded from the further analysis. These two\nsteps (estimation of noise variance and exclusion of blocks) are iteratively\nrepeated three times. For the verification of the proposed method, a new\nnoise-free test image database TAMPERE17 consisting of many highly textured\nimages is designed. It is shown for this database and different values of noise\nvariance from the set 25, 49, 100, 225, that the proposed method provides\napproximately two times lower estimation root mean square error than other\nmethods.\n