Removal of horizontal and vertical scratches for a new class of wavelet and log energy bounded Wiener estimation over Poisson-Gaussian noise model

Abstract

Generally Gaussian noise model is taken into account in image processing applications. However its presence in charge coupled device (CCD) enabled cameras. Off let photon noise as packet of energy came up from statistical nature of electromagnetic radiation from object of interest. Photon noise with poison distribution also exists with Gaussian noise. Certainly, Poisson-Gaussian noise model is often essential gradients for image processing to achieve acceptable visual appearance and strengthen the recipient signal. Proposed work demonstrated the Poisson-Gaussian noise model and its implementation in the new soft shrinkage algorithm, which is based on wavelet scaling coefficients and log energy transformation of detail coefficients in Wiener estimation. The significance of the proposed method in digital image processing is only and only for providing pure visual cortex of the recipient signal. Proposed work demonstrated the automated sub band adaptive statistical estimation of standard deviation of noisy wavelet coefficients. Further the proposed extended filter minimises the problem of horizontal and vertical scratches which arose in jointly processed wavelet transform and log energy-based Wiener filter. A significant improvement in PSNR using peak tube voltage was reported for guaranteed pure image restoration and also deployed structural similarity index (SSIM) for high quality visual assurance.