DCT-Based Color Image Denoising: Efficiency Analysis and Prediction

NN-Based Prediction of Sentinel-1 SAR Image Filtering Efficiency

“…For all 36 input parameters used jointly, a very good result is observed-RMSE = 0.019 and Adjusted R 2 is equal to 0.992 (recall here that SSIM4 varies within the limits from 0 to 1). A practically important advantage is that it is possible to apply fewer input parameters (combinations ## [33][34][35] to produce the same accuracy. Again, we can note combinations # 19 and 20 that employ only six and seven input parameters, respectively.…”

“…One more group of input parameters that has already recommended itself well in denoising efficiency prediction relates to DCT-coefficient probabilities as the result of their comparison to threshold (or thresholds). Expedience of using such parameters was previously demonstrated [23,24,35]. It has been proposed to analyze the probability that DCT coefficients in blocks do not exceed certain threshold(s).…”

“…The task of filtering efficiency prediction has attracted sufficient attention of researchers in recent ten years [21][22][23][24][34][35][36][37][38]. It has been shown in [21] that, having a noise-free image, it is possible to determine what is a potential output MSE for nonlocal filtering of this image if it is corrupted by additive white Gaussian noise (AWGN) with a given standard deviation or variance.…”

“…We have studied other methods to filtering efficiency prediction [24,[34][35][36][37][38]. Initial assumptions are the following:…”

“…It has been also demonstrated that it is worth using several input parameters jointly to improve prediction accuracy. The approach to prediction has been shown to be quite universal and applicable for color and multichannel images [35] as well as images corrupted by pure additive or pure multiplicative spatially correlated noises [23,36]. A prediction is possible not only for the filters based on DCT [40,41], but also for other modern filters [24] that employ other operation principles.…”