A denoising unit based on wavelet multiresolution analysis is added ahead of the multilayered perceptron. The cost function used in neural network learning is also applied as the denoising criterion and hence denoising itself is treated as a part of the integrated model. By introducing continuously derivable generalized soft thresholding function and infinite thresholds, a gradient based learni...

Fig. 3: FA maps from the original (left), and the denoised (right) DTI data set. Magnified views of a ROI (bottom) demonstrate feature preservation in fine structures. Fig. 1: A numerical example of spatially variant regularization. (a) A numerical test image. (b) Noisy test image. (c) TV denoising with λ=20. (d) TV denoising with λ=10. (e) λ map: λ=10 (dark region) and λ=20 (bright region). (f...

In this paper we propose the use of a new implementation of the hyperanalytic wavelet transform, (HWT), in association with a Maximum a Posteriori (MAP) filter named bishrink. Such a denoising method is sensitive to the selection of the mother wavelets used for the computation of the HWT. Taking into account the drawbacks of the bishrink filter and the sensibility with the selection of the moth...

In 1992 David Donoho has introduced the term denoising in connection with the adaptive nonlinear ...ltering in the discrete wavelets transform domain. Despite its advantages this method is not used yet in the communications ...eld. The goal of this paper is to propose a new denoising method, using another strategy for the threshold selection and another wavelets transform. This new discrete wav...

We propose a new framework for manifold denoising based on processing in the graph Fourier frequency domain, derived from the spectral decomposition of the discrete graph Laplacian. Our approach uses the Spectral Graph Wavelet transform in order to perform non-iterative denoising directly in the graph frequency domain, an approach inspired by conventional wavelet-based signal denoising methods....

In electron tomographic reconstructions of biological specimens the information about their structure is not directly accessible since most of the signal is buried in noise. An interpretation of the images using surface and volume rendering techniques is difficult due to the noise sensitivity of rendering algorithms. We explore the use of various multiscale representations for denoising 2D and ...

Denoising is an indispensable function for digital cameras. In respect that noise is diffused during the demosaicking, the denoising ought to work directly on bayer data. The difficulty of denoising on bayer image is the interlaced mosaic pattern of red, green, and blue. Guided filter is a novel time efficient explicit filter kernel which can incorporate additional information from the guidance...

This paper presents an efficient image denoising scheme by using principal component analysis (PCA) with local pixel grouping (LPG). For a better preservation of image local structures, a pixel and its nearest neighbors are modeled as a vector variable, whose training samples are selected from the local window by using block matching based LPG. Such an LPG procedure guarantees that only the sam...

In this paper, we develop a fast multilevel algorithm for simultaneously denoising and deblurring images under the total-variation regularization. Although much effort has been devoted to developing fast algorithms for the numerical solution and the denoising problem was satisfactorily solved, fast algorithms for the combined denoising and deblurring model remain to be a challenge. Recently sev...

We develop a new approach to image denoising based on complexity regularization. This technique presents a flexible alternative to the more conventional l, l, and Besov regularization methods. Different complexity measures are considered, in particular those induced by state– of–the–art image coders. We focus on a Gaussian denoising problem and derive a connection between complexity–regularized...