In many experiments, the electrochemiluminescence images captured by smartphones often have a lot of noise, which makes it difficult for researchers to accurately analyze the light spot information from the captured images. Therefore, it is very important to remove the noise in the image. In this paper, a Center-Adaptive Median Filter (CAMF) based on YOLOv5 is proposed. Unlike other traditional filtering algorithms, CAMF can adjust its size in real-time according to the current pixel position, the center and the boundary frame of each light spot, and the distance between them. This gives CAMF both a strong noise reduction ability and light spot detail protection ability. In our experiment, the evaluation scores of CAMF for the three indicators Peak Signal-to-Noise Ratio (PSNR), Image Enhancement Factor (IEF), and Structural Similarity (SSIM) were 40.47 dB, 613.28 and 0.939, respectively. The results show that CAMF is superior to other filtering algorithms in noise reduction and light spot protection.

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.