Jun Yang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Jun Yang has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Jun Li
College of Information Engineering, Sichuan Agricultural University
2 shared publications - Bing Yang
Agricultural Information Engineering Higher Institution Key Laboratory of Sichuan Province, Ya’an, China
1 shared publication - Guanyu Chen
Agricultural Information Engineering Higher Institution Key Laboratory of Sichuan Province, Ya’an, China
1 shared publication - Junyang Chen
College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China
1 shared publication - Shijie Dai
College of Science, Sichuan Agricultural University, Ya’an 625000, China
1 shared publication - Xinhang Jiang
College of Information Engineering Sichuan Agricultural University, Ya’an, China
1 shared publication
Latest Publications
- Open Access
- 19 June 2025
- Published by Springer Nature in Applied Intelligence
- Vol. 55 (11) , 1-15
CAMFv2: Better, faster and stronger for electrochemiluminescence image denoising
- 19 June 2025
- Published by Springer Nature in Applied Intelligence
- Vol. 55 (11) , 1-15
Open AccessCAMFv2: Better, faster and stronger for electrochemiluminescence image denoising
- Open Access
- 24 March 2023
- Published by MDPI AG in Electronics
- Vol. 12 (7) , 1544
An Improved Median Filter Based on YOLOv5 Applied to Electrochemiluminescence Image Denoising
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.
- 24 March 2023
- Published by MDPI AG in Electronics
- Vol. 12 (7) , 1544
Open AccessAn Improved Median Filter Based on YOLOv5 Applied to Electrochemiluminescence Image Denoising
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.