华南理工大学学报（自然科学版）2026，Vol.54Issue(5)：1-14,14.DOI:10.12141/j.issn.1000-565X.250231
基于双分支循环网络的足迹图像去噪方法
Denoising Method for Footprint Images Based on Dual-Branch Cyclic Network
摘要
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 reconstruc-tion.The overall network comprises two generators and two discriminators,with the generator comprising two syner-gistically 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.Simulta-neously,the color correction branch employs an adaptive Color Consistency Module(CCM),which extracts color fea-tures 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 de-tails while improving overall perceptual quality.Experimental evaluations conducted on the self-built footprint data-set,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.关键词
图像去噪/足迹图像/生成对抗网络/双分支循环网络Key words
image denoising/footprint image/generative adversarial network/dual-branch cyclic network分类
信息技术与安全科学引用本文复制引用
鲍文霞,佘成龙,王年,郭文涛..基于双分支循环网络的足迹图像去噪方法[J].华南理工大学学报（自然科学版）,2026,54(5):1-14,14.基金项目
福建省科技重大专项专题项目(2024HZ025022)Supported by Fujian Province Major Science and Technology Special Project(2024HZ025022) （2024HZ025022）
