A novel family of edge preserving anisotropic filters

Kilic, Haydar; Ceyhan, Salim; Gerek, Ömer Nezih

doi:10.1016/j.dsp.2022.103623

Cited by 1 publication

(1 citation statement)

References 22 publications

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“…At present, image-processing algorithms can be roughly divided into three categories: spatial-domain denoising, frequency-domain denoising [ 2 , 3 ], and deep-learning denoising. Spatial-domain denoising involves directly averaging pixels on noisy images, such as Gaussian filtering [ 4 ], median filtering [ 5 ], and anisotropic filtering [ 6 ]. This type of method can effectively preserve the edge information of the image, but it is also prone to blurring the 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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