1 Corresponding author. Email: [email protected]. This research is supported by the Hong Kong General Research Fund (PolyU 5330/07E) and the National Science Foundation Council of China under Grant no. 60634030. Abstract – Most of the existing image interpolation schemes assume that the image to be interpolated is noise free. This assumption is invalid in practice because noise will be...

introduction: in this study the evaluation of a platelet-based maximum penalized likelihood estimation (mple) for denoising spect images was performed and compared with other denoising methods such as wavelets or butterworth filtration. platelet-based mple factorization as a multiscale decomposition approach has been already proposed for better edges and surfaces representation due to poisson n...

Denoising or usually known as noise reduction is one of the most essential processes for digital image processing. The goal of denoising is how to remove the noise while keeping the important image features as much as possible. In this paper, a denoising algorithm using adaptive block-based singular value decomposition filtering is presented. The proposed approach, instead of applying block-bas...

Image processing is any form of signal processing for which the input is an image, such as a photograph or video frame; the output of image processing may be either an image or a set of characteristics or parameters related to the image. Image denoising refers to the recovery of a digital image that has been contaminated by additive white Gaussian noise. In Existing a patch-based Wiener filter ...

In this article we show that when an image is corrupted by additive noise, its curvature image is less affected by it, i.e. the PSNR of the curvature image is larger. We conjecture that, given a denoising method, we may obtain better results by applying it to the curvature image and then reconstructing from it a clean image, rather than denoising the original image directly. Numerical experimen...

This paper is devoted to the total variation (TV) based approach to image denoising and restoration. The restored image minimizes total variation in the class of images which satisfy the contraints given by the image acquisition model. We compute some explicit solutions of the denoising model which explain some of the features observed in numerical experiments. We also comment on some alternati...

Removal of noise from an image is an extensively studied problem in image processing. Indeed, the 5 recent advent of sophisticated and highly effective denoising algorithms lead some to believe that 6 existing methods are touching the ceiling in terms of noise removal performance. Can we leverage 7 this impressive achievement to treat other tasks in image processing? Recent work has answered th...

A natural scene statistics (NSS) based blind image denoising approach is proposed, where denoising is performed without knowledge of the noise variance present in the image. We show how such a parameter estimation can be used to perform blind denoising by combining blind parameter estimation with a state-of-the-art denoising algorithm. Our experiments show that for all noise variances simulated...

Partial differential equation has a remarkable effect on image denoising, compression and segmentation. Based on partial differential equations, the denoising experiment is carried out on those artistic images requiring high degree of visual reduction through the application of 3 image-denoising algorithm models including thermal diffusion equation, P-M diffusion equation and the TV diffusion e...

Recently Burger et al. (2012) and Xie et al. (2012) proposed to use a denoising autoencoder (DAE) for denoising noisy images. They showed that a plain, deep DAE can denoise noisy images as well as the conventional methods such as BM3D and KSVD. Both of them approached image denoising by denoising small, image patches of a larger image and combining them to form a clean image. In this setting, i...