Link of paper :
Due to the imperfection of image acquisition systems and transmission channels, images are often corrupted by noise. This degradation leads to a signiﬁcant reduction of image quality and then makes more difﬁcult to perform high-level vision tasks such as recognition, 3-D reconstruction, or scene interpretation. The image denoising is important, not only because of the evident applications it serves. Being the simplest possible inverse problem, it provides a convenient platform over which image processing ideas and techniques can be assessed. In most cases, this corruption is commonly modeled by a zero-mean additive white Gaussian random noise.
Over the last decade, plenty of image-denoising methods exist, originating from various disciplines such as probability theory, statistics, partial differential equations, linear and nonlinear ﬁltering, and spectral and multiresolution analysis (Liu et al., 2008). All these methods rely on some explicit or implicit assumptions about the true (noise-free) signal in order to separate it properly from the random noise. In particular, the transform- domain denoising methods typically assume that the true signal can be well approximated by a linear combination of few basis elements (Dabov et al., 2007). That is, the signal is sparsely represented in the transform domain. Hence, by preserving the few high-magnitude transform coefﬁcients that convey mostly the true-signal energy and discarding the rest, which are mainly due to noise, the true signal can be effectively estimated. The sparsity of the representation depends on both the transform and the true-signal’s properties.
output code :
peaksnr before = 23.659551097946633
snr before = 13.786858445526661
peaksnr after = 27.636168606872697
snr after = 17.763475954452726
ssimValues before = 0.221353701319673
ssimValues after = 0.459691527385873
Input image :
Noisy image :
output image :
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