@article{BAO2026, 
author = {Wenxia BAO and Chenglong SHE and Nian WANG and Wentao GUO},
title = {Denoising Method for Footprint Images Based on Dual-Branch Cyclic Network},
year = {2026},
journal = {Journal of South China University of Technology (Natural Science Edition)},
volume = {54},
number = {5},
pages = {1-14},
keywords = {image denoising, footprint image, generative adversarial network, dual-branch cyclic network},
url = {https://www.sciopen.com/article/10.12141/j.issn.1000-565X.250231},
doi = {10.12141/j.issn.1000-565X.250231},
abstract = {As a key individual feature in forensic investigation and biometric recognition, footprint images are highly susceptible to diverse environmental factors during acquisition, often accompanied by complex noise and image quality degradation. To address composite noise commonly present in footprint images, this paper proposes an enhanced dual-branch cyclic denoising network for high-fidelity image restoration and texture structure reconstruction. The overall network comprises two generators and two discriminators, with the generator comprising two synergistically optimized branches: a denoising mapping branch and a color correction branch. The denoising mapping branch incorporates an Enhanced Multi-Scale Structure Block (EMSB) to strengthen structural modeling and texture recovery capabilities. By integrating multi-scale convolutions, depthwise separable convolutions, and multi-attention mechanisms, this branch effectively enhances feature representation in texture-sensitive regions. Simultaneously, the color correction branch employs an adaptive Color Consistency Module (CCM), which extracts color features via multi-scale residual convolutions and performs channel-wise normalization and residual fusion in the RGB space to suppress color deviation in the generated images. Furthermore, a multi-level structural perception loss function is designed, combining pixel-level accuracy with structural similarity to guide the network in recovering details while improving overall perceptual quality. Experimental evaluations conducted on the self-built footprint dataset, FSD-Real, demonstrate that the proposed method achieves a Peak Signal-to-Noise Ratio (PSNR) of 30.3 dB and a Structural Similarity Index (SSIM) of 0.926, significantly outperforming existing mainstream methods. Moreover, the method exhibits superior denoising performance and detail preservation in terms of subjective visual quality, validating its application potential in real-world footprint image processing tasks.}
}