Image restoration and denoising using block-based singular-value derivative
Min Wang, Shudao Zhou · Remote Sensing Letters · 2020
Singular-value decomposition (SVD) can be used to estimate the point spread function (PSF) of a blurred image to denoise the image, but it does not consider the local difference of the image; moreover, the selection of recombination order or rank is related to the estimation of the PSF and the recovery effect. An optical image restoration and denoising method with white Gaussian noise using the block-based singular-value derivative is proposed in this paper. First, PSF is estimated using the average energy spectrum of the singular-value vector of the ideal image, which is based on SVD properties, starting from the discrete degenerate model. Next, the inverse filtering is used to restore the image. Finally, the denoised image is obtained using block-based SVD filtering and combination, in which the singular-value recombination order is determined by the singular-value derivative. The experimental results demonstrate that the proposed method has a higher PSNR (peak signal-to-noise ratio) than other methods, and it can get a more clear image.