Denoising algorithms based on gradient dependent energy functionals, such as Perona-Malik and total variation denoising, modify images towards piecewise constant functions. Although edge sharpness and location is well preserved, important information, encoded in image features like textures or certain details, is often compromised in the process of denoising. We propose a mechanism that better ...

This paper presents a novel approach to image denoising using adaptive principal components. Our assumptions are that the image is corrupted by additive white Gaussian noise. The new denoising technique performs well in terms of image visual fidelity, and in terms of PSNR values, the new technique compares very well against some of the most recently published denoising algorithms.

In this paper, fusion and denoising algorithm for more than two multifocus images is presented. For denoising of more than two multifocus images, concept of minimizing weighted energy function is adapted. For fusion of multifocus images some weighted function are calculated and used. In pixel domain denoising is carried out by using total variation method.

We propose a soft thresholding approach to the minimum description length wavelet denoising. Our method is based on combining two-part coding with normalized maximum likelihood universal models to give a soft thresholding denoising criterion. Experiments with the proposed MDL soft thresholding method indicate that our denoising criterion leads to fairly similar performance as with the well-know...

Denoising can be used as a tool to enhance image quality and enforce low radiation doses in X-ray medical imaging. The effectiveness of denoising techniques relies on the validity of the underlying noise model. In full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT), calibration steps like the detector offset and flatfielding can affect some assumptions made by most deno...

The need for image enhancement and restoration is encountered in many practical applications. For instance, distortion due to additive white Gaussian noise (AWGN) can be caused by poor quality image acquisition, images observed in a noisy environment or noise inherent in communication channels. In this thesis, image denoising is investigated. After reviewing standard image denoising methods as ...

The denoising of video data should take into account both temporal and spatial dimensions, however, true 3D transforms are rarely used for video denoising. Separable 3-D transforms have artifacts that degrade their performance in applications. This paper describes the design and application of the non-separable oriented 3-D dual-tree wavelet transform for video denoising. This transform gives a...

This paper presents a new signal denoising method based on the classical three step procedure analysis-thresholdsynthesis and the Spectral Intrinsic Decomposition (SID). This method consists of an iterative thresholding of the SID components. If the wavelets denoising approach depends on the choice of the wavelet form, the SID-denoising proposed in this paper is self adaptive. The SID-based rem...

The denoising of a natural image corrupted by noise is a classical problem in image processing. In this paper, an efficient algorithm of image denoising based on multi-objective optimization in discrete wavelet transform (DWT) domain is proposed, which can achieve the Pareto optimal wavelet thresholds. First, the multiple objectives for image denoising are presented, then the relation between t...

Many state-of-the-art denoising algorithms focus on recovering high-frequency details in noisy images. However, images corrupted by large amounts of noise are also degraded in the lower frequencies. Thus properly handling all frequency bands allows us to better denoise in such regimes. To improve existing denoising algorithms we propose a meta-procedure that applies existing denoising algorithm...