This paper introduces a new approach to non-local means image denoising. Instead of using all pixels located in the search window for estimating the value of a pixel, we identify the highly corrupted pixels and assign less weight to these pixels. This method is called robust non-local means. Numerical and subjective evaluations using ultrasound images show good performances of the proposed deno...

In this paper, a new image denoising method based on wavelet analysis and support vector machine regression (SVR) is presented. The feasibility of image denoising via support vector regression is discussed and an illustrative example is given. The wavelet kernel is proposed to construct wavelet support vector machine (WSVM). The result of experiment shows that the denoising method based on WSVM...

In this paper a wavelet shrinkage algorithm based on fuzzy logic is proposed to improve the tea leaf image. The Tea Leaf images are normally changes to unclear images by the presence of noise, low or high dissimilarity both in the edge area and also in the image area. The Fuzzy shrink is used to enhance the image. In exacting, intra-scale dependency within wavelet coefficients is modeled using ...

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...

In this paper, the author researched on the model of image denoising based on the fusion of anisotropic diffusion and total variation models. The noise is present at almost all data. The noise can degrade image quality, as a result the interpretations and analysis of the image will be much harder. Denoising is the process of reducing the noise.

A new filtering technique is proposed to denoising process on digital images. This filter is a combination of statistics and average. It is very useful for denoising if image is corrupted with impulse noise and gaussian noise. This filtering scheme offers edge and fine detail preservation performance while, at the same time, effectively denoising digital images. Extensive simulation results wer...

This paper presents some image processing techniques that can be used for radiographic image enhancement. Contrast enhancement, filtering, denoising, and interpolation processes are carried out in this paper. Contrast enhancement is carried out using adaptive histogram equalization. Filtering is carried out using median, Wiener, Lee, and Kuan filters. Wavelet and curvelet transforms are used fo...

This paper proposes a novel image denoising technique based on the normal inverse Gaussian (NIG) density model using an extended non-negative sparse coding (NNSC) algorithm. Here, we demonstrate that the NIG density provides a very good fitness to the non-negative sparse data. In denoising process, by exploiting a NIG-based maximum a posteriori estimator (MAP) of an image corrupted by additive ...

Images often contain noise due to imperfections of image acquisition techniques. Noise should be removed from images so that the details of image objects (e.g., blood vessels, inner foldings, or tumors in human brain) can be clearly seen, and the subsequent image analyses are reliable. With broad usage of images in many disciplines like medical science, image denoising has become an important r...

Due to the disadvantage of large amounts of data computation and image quality degradation of classical reconstruction algorithm, a novel adaptive method of image reconstruction denoising based on compressive sensing is proposed. Firstly, the wavelet approximate coefficients and detail coefficients from the image noise are Gaussian distribution, and have different variances in different levels....