We revisit total variation denoising and study an augmented model where we assume that an estimate of the image gradient is available. We show that this increases the image reconstruction quality and derive that the resulting model resembles the total generalized variation denoising method, thus providing a new motivation for this model. Further, we propose to use a constraint denoising model a...

Underperformance in higher frequency signal regions denoising is a common problem for many denoising methods. Wavelet transforms are, generally, less prone to the problem than the pure spatial or frequency domain transforms, but there is still much room for improvements. In this paper, we propose a point-wise adaptive wavelet transform for signal denoising applications. It is very efficient in ...

Wavelet shrinkage denoising methods are widely used for estimation of biological signals from noisy environment. The popular Hard and Soft thresholding filters are commonly used in these methods. In this paper shrinkage method based on a New Thresholding filter for denoising of biological signals is proposed. The efficacy of this filter is evaluated by applying this filter for denoising of ECG ...

The quality of biomedical image is degraded by many sources of noise such as imperfect instruments, interference from natural phenomena and data acquisition errors. In Ultrasound and MRI images, the image quality is degraded mainly by Speckle noise and Rician noise respectively. Removing noise from biomedical image is still a challenging problem for the biomedical researchers and lots of denois...

Most existing Digital Color Cameras use a Single -sensor with a color filter array (CFA) to capture images. The quality of demosaicked images is degraded due to the sensor noise introduced during the image acquisition process. The conventional solution to combating CFA sensor noise is demosaicking first, followed by a separate denoising processing. This strategy will generate many noise-caused ...

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

Patch-based denoising methods have recently emerged due to its good denoising performance. In this paper, based on analysis of the optimal over-complete patch aggregation, we highlight the importance of a local transform for good image features representation. A finite Radon transform (FRAT) based two-stage over-complete image denoising algorithm is then proposed for obtaining good visual quali...

New imaging capabilities have given rise to higher dimensional image processing. This paper presents a generalization of total variation (TV) based denoising with specific application to three-dimensional flash lidar imagery. The generalization uses a weighted norm, rather than the standard Euclidian measure, that accounts for sampling differences that may exist along different axes. We compare...

Wavelet transforms enable us to represent signals with a high degree of sparsity. This is the principle behind a non-linear wavelet based signal estimation technique known as wavelet denoising. In this report we explore wavelet denoising of images using several thresholding techniques such as SUREShrink, VisuShrink and BayesShrink. Further, we use a Gaussian based model to perform combined deno...