安徽大学 电子信息工程学院，安徽 合肥 230601

关键词: 图像去噪, 足迹图像, 生成对抗网络, 双分支循环网络

Abstract:

As a critical biometric feature in criminal investigation and biometric recognition, footprint images are highly susceptible to various environmental interferences during acquisition, often accompanied by complex noise and degradation in image quality. To address the prevalent compound noise in footprint images, this paper proposes an improved dual-branch cyclic denoising network capable of high-fidelity image restoration and texture structure reconstruction. The overall framework consists of two generators and two discriminators. The generator is composed of two synergistically optimized branches: a denoising mapping branch and a color correction branch. Specifically, the denoising mapping branch incorporates an Enhanced Multi-Scale Structure Block (EMSB) to strengthen structural modeling and texture recovery. This module integrates multi-scale convolution, depthwise separable convolution, and multiple attention mechanisms to enhance feature representation in texture-sensitive regions. The color correction branch introduces an adaptive Color Consistency Module (CCM), which extracts color features through multi-scale residual convolution and performs channel-wise normalization and residual fusion in the RGB space to suppress color distortion in generated images. Furthermore, a Multi-Stage Structure-Perception Loss (MSSP-Loss) is designed, combining pixel-level accuracy with structural similarity to guide the network in recovering fine details while enhancing perceptual quality. Experiments conducted on the self-constructed footprint dataset FSD-Real demonstrate that the proposed method achieves a PSNR of 30.3 dB and an SSIM of 0.926, outperforming existing state-of-the-art methods. In addition, the method shows superior denoising performance and detail preservation in subjective visual evaluations, validating its potential for practical applications in real-world footprint image processing tasks.

Key words: image denoising, footprint image, generative adversarial network, dual-branch cyclic network