BM3D Denoising Algorithm with Adaptive Block-Match Thresholds

Zhang, You Sai; Zhu, Shu; Li, Yuan Jiang

doi:10.4028/www.scientific.net/amm.229-231.1715

Cited by 5 publications

(3 citation statements)

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“…Refs. [ 23 , 24 ] proposed an improved BM3D technology based on an adaptive threshold, which has excellent denoising performance for different types of noise in different images. In [ 25 ], to achieve more accurate block-matching, each image is divided into three different regions using the region energy of the alternating current (AC) coefficients of discrete cosine transform (DCT), and the block size is adaptively selected based on the region.…”

Section: Introductionmentioning

confidence: 99%

An Improved BM3D Algorithm Based on Image Depth Feature Map and Structural Similarity Block-Matching

Cao,

Qiang,

Lin

et al. 2023

Sensors

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We propose an improved BM3D algorithm for block-matching based on UNet denoising network feature maps and structural similarity (SSIM). In response to the traditional BM3D algorithm that directly performs block-matching on a noisy image, without considering the deep-level features of the image, we propose a method that performs block-matching on the feature maps of the noisy image. In this method, we perform block-matching on multiple depth feature maps of a noisy image, and then determine the positions of the corresponding similar blocks in the noisy image based on the block-matching results, to obtain the set of similar blocks that take into account the deep-level features of the noisy image. In addition, we improve the similarity measure criterion for block-matching based on the Structural Similarity Index, which takes into account the pixel-by-pixel value differences in the image blocks while fully considering the structure, brightness, and contrast information of the image blocks. To verify the effectiveness of the proposed method, we conduct extensive comparative experiments. The experimental results demonstrate that the proposed method not only effectively enhances the denoising performance of the image, but also preserves the detailed features of the image and improves the visual quality of the denoised image.

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Section: Introductionmentioning

confidence: 99%

An Improved BM3D Algorithm Based on Image Depth Feature Map and Structural Similarity Block-Matching

Cao,

Qiang,

Lin

et al. 2023

Sensors

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“…PCA on adaptive-shape neighborhoods was proposed in [5]. A method applying adaptive block-match thresholds is presented in [6]. [7] changes BM3D algorithm parameter values according to noise level.…”

Section: Introductionmentioning

confidence: 99%

Image denoising with multiple layer block matching and 3D filtering

Fan

2014

SPIE Proceedings

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Block Matching and 3-D Filtering (BM3D) algorithm is currently considered as one of the most successful denoising algorithms. Despite its excellent results, BM3D still has room for improvements. Image details and sharp edges, such as text in document images are challenging, as they usually do not produce sparse representations under the linear transformations. Various artifacts such as ringing and blurring can be introduced as a result.This paper proposes a Multiple Layer BM3D (MLBM3D) denoising algorithm. The basic idea is to decompose image patches that contain high contrast details into multiple layers, and each layer is then collaboratively filtered separately.The algorithm contains a Basic Estimation step and a Final Estimation step. The first (Basic Estimation) step is identical to the one in BM3D. In the second (Final Estimation) step, image groups are determined to be single-layer or multilayer. A single-layer group is filtered in a same manner as in BM3D. For a multi-layer group, each image patch within the group is decomposed with the three-layer model. All the top layers in the group are stacked and collaboratively filtered. So are the bottom layers. The filtered top and bottom layers are re-assembled to form the estimation of the blocks. The final estimation of the image is obtained by aggregating estimations of all blocks, including both single-layer and multi-layer ones. The proposed algorithm shows excellent results, particularly for the images containing high contrast edges.

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“…These changes achieved better denoising performance, nevertheless they failed to make the algorithm continuous. BM3D based on adaptive threshold was proposed in 2012 which avoided modifying many parameters and was proved to be effective [6][7].…”

Section: Introductionmentioning

confidence: 99%