An Improved BM3D Method for eDNA Mieroarray Image Denoising

Shao, Guifang; Gao, Yubo; Zuo, Jiayu; Yue, Yuqing; Yang, Jian‐Bo

doi:10.1109/iccse.2018.8468760

Cited by 1 publication

(1 citation statement)

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“…To further improve the performance of the denoising algorithm, in 2007, Dabov, Foi et al [ 9 ] proposed the BLOCK-MATCHING and 3D filtering (BM3D) algorithm that has obvious denoising effects and can effectively retain image details, combining the characteristics of nonlocal self-similarity and frequency-domain denoising. The BM3D algorithm can be simply summarized into three steps [ 10 ]: the first step is block-matching grouping, which involves dividing the noisy image into blocks. Based on certain similarity measurement criteria and set threshold, search for similar blocks of each block and combine them to form a 3D matrix cluster; The second step is to use a filtering method to filter each cluster; The third step is aggregation, which involves aggregating the filtered clusters to obtain the output image.…”

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%