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Sequential dual generative adversarial network for snowflake noise elimination
- 1 School of Computer Science, Zhongyuan University of Technology, Zhengzhou, China
- 2 School of Automation, Hangzhou Dianzi University, Hangzhou, China
- 3 School of Computer Science, Zhongyuan University of Technology, Zhengzhou, China
- 4 Zhongyuan University of Technology
* Author to whom correspondence should be addressed.
Abstract
Aiming at the problem of snowflake occlusion and haze veil effect in the collected images by the vehicle-mounted camera for the influence of snowflakes and haze. The decoupling and double-supervised weather elimination network was proposed based on the sequential reconstruction of high-frequency and low-frequency components in background images. Decoupling the desnow and dehaze assignment to two sub-networks sequentially, the network filters the high-frequency feature vectors in the image background region by the eigenvalues of the convolution kernel in the spatial domain, and then the second subnetwork performs image coloring and edge fine-tuning tasks based on edge context features obtained by generative adversarial networks. The algorithm has tested on the SRRS-6000 dataset, which verified the effectiveness in the significant performance on noise removal. The Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) has reached 33.29dB/0.94, 32.8dB/0.9316, 30.13dB/0.93, 25.88dB/0.82 on Snow100K-S､Snow100K-M､Snow100K-L､I&O-Haze dataset, respectively. Experiments have shown that the decoupling and double supervised method have efficient snowflake and haze removal performance in image denoising tasks, and the adaptability of unmanned assistance systems under complex weather conditions has been enhanced.
Keywords
Snow Removal, Generative, Generative adversarial Network, Sequential Network
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Cite this article
Li,Y.;Bao,X.;Qiang,Z.;Yang,G. (2025). Sequential dual generative adversarial network for snowflake noise elimination. Theoretical and Natural Science,95,18-30.
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