Group sparsity or nonlocal image representation has shown great potential in image denoising. However, most existing methods only consider the nonlocal self-similarity (NSS) prior of noisy input image, that is, the similar patches collected only from degraded input, which makes the quality of image denoising largely depend on the input itself. In this paper we propose a new prior model for imag...

With progress in radar systems, a number of methods have been developed for signal processing and detection in radars. A number of modern radar signal processing methods use time-frequency transforms, especially the wavelet transform (WT) which is a well-known linear transform. The interference canceling is one of the most important applications of the wavelet transform. In Ad-hoc detection met...

Array-based comparative genome hybridization (array CGH) is a recently developed high-throughput technique to detect DNA copy number aberrations. Typically, array CGH data is noisy. Wavelet denoising was previously shown to have superior performance for denoising array CGH data. However, the effect of different signal extensions methods on the performance of wavelet denoising in this particular...

This paper proposes an efficient analysis of Ultrasound Image Denoising using different type of filtering methods. The search for efficient image denoising methods is still a valid challenge at the crossing of functional analysis and statistics. Image denoising has become an essential exercise in medical imaging especially the ultrasound image. This paper proposes a medical image denoising algo...

The contourlet transform has the benefit of efficiently capturing the oriented geometrical structures of images. In this paper, by incorporating the ideas of Stein’s Unbiased Risk Estimator (SURE) approach in Nonsubsampled Contourlet Transform (NSCT) domain, a new image denoising technique is devised. We utilize the characteristics of NSCT coefficients in high and low subbands and apply SURE sh...

The use of wavelets in denoising, seems to be an advantage in representing well the details. However, the edges are not so well preserved. Total variation technique has advantages over simple denoising techniques such as linear smoothing or median filtering, which reduce noise, but at the same time smooth away edges to a greater or lesser degree. In this paper, an efficient denoising method bas...

Wiener filter suppresses noise efficiently. However, it makes the out image blurred. Curvelet preserves the edges of natural images perfectly, but, it produces visual distortion artifacts and fuzzy edges to the restored image, especially in homogeneous regions of images. In this paper, a new image denoising framework based on Curvelet transform and wiener filter is proposed, which can stop nois...