In this work a new version of block-matching and 3D filtering (BM3D) denoising approach introduced by Dabov et al. for denoising the image corrupted by additive white Gassian noise is proposed. The BM3D performs collaborative filtering to the 3D image groups composed by similar image blocks with the fixed hard-thresholding operator. The proposed version of BM3D adopts adaptive block-matching th...

The block-matching with 3D transform domain collaborative filtering (BM3D) achieves very good performance in image denoising. However, BM3D becomes ineffective when an image is heavily contaminated by noise. This is because it allows block-matching to search out of the region where a template block is located, resulting in poor matching. To address this, this paper proposes a bounded BM3D schem...

This paper proposes a new procedure in order to improve the performance of block matching and 3-D filtering (BM3D) image denoising algorithm. It is demonstrated that it is possible to achieve a better performance than that of BM3D algorithm in a variety of noise levels. This method changes BM3D algorithm parameter values according to noise level, removes prefiltering, which is used in high nois...

We propose an image restoration technique exploiting regularized inversion and the recent block-matching and 3D filtering (BM3D) denoising filter. The BM3D employs a non-local modeling of images by collecting similar image patches in 3D arrays. The so-called collaborative filtering applied on such a 3D array is realized by transformdomain shrinkage. In this work, we propose an extension of the ...

Image denoising is an important pre-processing step in most imaging applications. Block Matching and 3D Filtering (BM3D) is considered to be the current state-of-art algorithm for additive image denoising. But this algorithm uses a fixed hard thresholding scheme to attenuate noise from a 3D block. Experiments show that this fixed hard thresholding deteriorates the performance of BM3D because it...

BM3D frames based formulation provides new perspectives for the use of BM3D modeling in the circular shift-invariant reconstruction, but cannot be applied in the non-variant reconstruction. In order to solve this problem, a radial blur physical model was established, in this paper rectangular block replaced sector block in rectangular coordinate image, the matching area and restoration coeffici...

SAR-BM3D is one of the state of the art despeckling algorithms for SAR images. However, when tackling with high resolution SAR images, it often has an unsatisfying despeckling performance in the homogeneous smooth regions, together with a high time complexity. In this paper, a novel downsampled SAR-BM3D despeckling approach combined with edge compensation is proposed. The proposed algorithm con...

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 ...

Spatially adaptive nonlocal patch-wise estimation is one of the most promising recent directions in image processing. Within this framework, a number of Block Matching 3-D filtering (BM3D) algorithms have been developed for different imaging problems [1], [2], [3]. In this paper we present the analysis/synthesis frames for BM3D image modeling and use them to develop novel recursive deblurring a...

Recently, collaborative image filtering based on groupbased sparse representation has gained a popularity in image restoration. BM3D frame [1], one of the first example of such a representation, utilizes both local sparsity of small size image patches and group-sparsity of collections of selfsimilar image patches. As a sparsifying transforms in the spatial and similarity domains, fixed transfor...