Shape-Adaptive Transforms Filtering


EXPERIMENT RESULTS

(“Shape-Adapted” DCT)

The results in this page have been obtained by replacing the SA-DCT with the more complex “Shape-Adapted” DCT (obtained by orthogonalization). Such results are only marginally better than those obtained with the SA-DCT, and visually the two estimates are almost indistinguishable.





Denoising


Anisotropic LPA-ICI + Gilge's “Shape-Adapted” DCT denoising
Results (PSNR, dB), noise is white additive Gaussian with variance σ².
(Click on the numbers to see the corresponding images)
σLena
512x512
Boats
512x512
House
256x256
Peppers
256x256
Cameraman
256x256
Barbara
512x512
Hill
512x512
Lake
512x512
5 38.62 37.17 39.45 38.14 38.26 37.57 37.04 36.62
10 35.71 33.69 36.09 34.64 34.12 33.50 33.44 32.86
15 34.00 31.85 34.26 32.62 31.84 31.30 31.60 31.06
20 32.75 30.54 33.01 31.19 30.32 29.84 30.40 29.82
25 31.76 29.52 32.01 30.07 29.22 28.75 29.51 28.87
30 30.95 28.68 31.15 29.16 28.33 27.87 28.81 28.10
35 30.25 27.98 30.42 28.41 27.59 27.10 28.24 27.44
50 28.69 26.36 28.78 26.75 25.94 24.94 26.93 25.94
75 26.78 24.70 26.64 24.71 23.92 23.30 25.50 24.26
100 25.44 23.58 25.08 23.30 22.53 22.38 24.49 23.06





Deblurring


Pointwise “Shape-Adapted” DCT regularized deconvolution
Results (ISNR, dB) for four different deblurring experiments: (Click on the numbers to see the corresponding images)
Experiment #1234
Anisotropic LPA-ICI + Gilge's “Shape-Adapted” DCT8.588.296.344.55

Experiment 1 - Cameraman 256x256, blur: 9x9 uniform "box-car" blur, Gaussian white noise: BSNR 40dB (σ²=0.308)
Experiment 2 - Cameraman 256x256, blur: 15x15 1/(x²+y²), x,y=-7,...,7, kernel, Gaussian white noise: σ²=2
Experiment 3 - Cameraman 256x256, blur: 15x15 1/(x²+y²), x,y=-7,...,7, kernel, Gaussian white noise: σ²=8
Experiment 4 - Lena 512x512, blur: 5x5 separable [1, 4, 6, 4, 1]/16 filter, Gaussian white noise: σ²=49









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