扩散双人行：通过小波抑制和谐双通道图像分离
The Diffusion Duet: Harmonizing Dual Channels with Wavelet Suppression for Image Separation
AI总结：
本文提出DCDSM模型，通过小波抑制模块和双通道扩散模型提升图像分离性能，在雨雪去除和复杂混合分离中取得优异效果。
摘要：
盲图像分离（BIS）指的是在未知混合模式和没有源图像先验知识的条件下，从单个观测图像中同时估计和恢复多个独立源图像的逆问题。传统方法依赖于统计独立性假设或CNN/GAN变体，在真实场景中难以表征复杂的特征分布，导致在强噪声和非线性混合条件下出现估计偏差、纹理失真和伪影残留。本文创新性地将扩散模型引入双通道BIS，提出了一种高效的双通道扩散分离模型（DCDSM）。DCDSM利用扩散模型强大的生成能力来学习源图像特征分布并有效重建特征结构。在双分支反向去噪过程中，设计了一个新颖的小波抑制模块（WSM），形成一个交互式分离网络，通过利用源图像之间的互耦噪声特性来增强细节分离。在包含雨/雪和复杂混合的合成数据集上进行了大量实验，结果表明DCDSM在图像恢复任务中获得了PSNR/SSIM值分别为35.0023 dB/0.9549和29.8108 dB/0.9243的雨雪去除性能，优于Histoformer和LDRCNet；在复杂混合分离中，恢复的双源图像平均PSNR和SSIM分别为25.0049 dB和0.7997，优于对比方法。主观和客观评估均确认DCDSM在解决雨/雪残留去除和细节保留挑战方面的优越性。
英文摘要：
Blind image separation (BIS) refers to the inverse problem of simultaneously estimating and restoring multiple independent source images from a single observation image under conditions of unknown mixing mode and without prior knowledge of the source images. Traditional methods relying on statistical independence assumptions or CNN/GAN variants struggle to characterize complex feature distributions in real scenes, leading to estimation bias, texture distortion, and artifact residue under strong noise and nonlinear mixing. This paper innovatively introduces diffusion models into dual-channel BIS, proposing an efficient Dual-Channel Diffusion Separation Model (DCDSM). DCDSM leverages diffusion models' powerful generative capability to learn source image feature distributions and reconstruct feature structures effectively. A novel Wavelet Suppression Module (WSM) is designed within the dual-branch reverse denoising process, forming an interactive separation network that enhances detail separation by exploiting the mutual coupling noise characteristic between source images. Extensive experiments on synthetic datasets containing rain/snow and complex mixtures demonstrate that DCDSM achieves state-of-the-art performance: 1) In image restoration tasks, it obtains PSNR/SSIM values of 35.0023 dB/0.9549 and 29.8108 dB/0.9243 for rain and snow removal respectively, outperforming Histoformer and LDRCNet by 1.2570 dB/0.9272 dB (PSNR) and 0.0262/0.0289 (SSIM) on average; 2) For complex mixture separation, the restored dual-source images achieve average PSNR and SSIM of 25.0049 dB and 0.7997, surpassing comparative methods by 4.1249 dB and 0.0926. Both subjective and objective evaluations confirm DCDSM's superiority in addressing rain/snow residue removal and detail preservation challenges.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
