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<p>Electrochemiluminescence (ECL) is a phenomenon in which light is emitted when an electrochemical reaction occurs in a solution. ECL images are widely used in various fields. However, ECL images often carry considerable unavoidable noise. Prior to using ECL images, denoising it is very important. At present, the simplest and most valuable image denoising method is the center adaptive median filter (CAMF). However, in some images, CAMF does not work very well. There are three main kinds of images: images with excessive noise areas in the light spot, images with large differences in the size of the light spot, and images with different shapes of light spots. To make the CAMF problems easier to solve, we narrow the decision criteria for the current pixels inside and outside the spot, the selection of the filter size threshold, the selection of the nearest spot, the determination of the spot center, etc., and we then propose CAMFv2. Our experiments show that our CAMFv2 is better than CAMF.</p>

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CAMFv2: Better, faster and stronger for electrochemiluminescence image denoising

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月19日

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  • Jun Li,
  • Jun Yang,
  • Xinhang Jiang,
  • Bing Yang,
  • Guanyu Chen

摘要

Electrochemiluminescence (ECL) is a phenomenon in which light is emitted when an electrochemical reaction occurs in a solution. ECL images are widely used in various fields. However, ECL images often carry considerable unavoidable noise. Prior to using ECL images, denoising it is very important. At present, the simplest and most valuable image denoising method is the center adaptive median filter (CAMF). However, in some images, CAMF does not work very well. There are three main kinds of images: images with excessive noise areas in the light spot, images with large differences in the size of the light spot, and images with different shapes of light spots. To make the CAMF problems easier to solve, we narrow the decision criteria for the current pixels inside and outside the spot, the selection of the filter size threshold, the selection of the nearest spot, the determination of the spot center, etc., and we then propose CAMFv2. Our experiments show that our CAMFv2 is better than CAMF.