An Improved Median Filter Based on YOLOv5 Applied to Electrochemiluminescence Image Denoising

Abstract

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.