Image denoising aims to remove noise while preserving structural details and perceptual realism, yet distortion-driven methods often produce over-smoothed reconstructions, especially under strong noise and distribution shift. This paper proposes a generative compression framework for perception-based denoising, where restoration is achieved by reconstructing from entropy-coded latent representations that enforce low-complexity structure, while generative decoders recover realistic textures via perceptual measures such as learned perceptual image patch similarity (LPIPS) loss and Wasserstein distance. Two complementary instantiations are introduced: (i) a conditional Wasserstein GAN (WGAN)-based compression denoiser that explicitly controls the rate-distortion-perception (RDP) trade-off, and (ii) a conditional diffusion-based reconstruction strategy that performs iterative denoising guided by compressed latents. We further establish non-asymptotic guarantees for the compression-based maximum-likelihood denoiser under additive Gaussian noise, including bounds on reconstruction error and decoding error probability. Experiments on synthetic and real-noise benchmarks demonstrate consistent perceptual improvements while maintaining competitive distortion performance.

翻译：图像去噪旨在去除噪声的同时保留结构细节与感知真实性，然而基于失真的方法往往产生过度平滑的重建结果，尤其在强噪声和分布偏移条件下更为明显。本文提出一种基于感知去噪的生成式压缩框架，其通过从熵编码的隐式表示中重建图像来实现复原——该隐式表示强制编码低复杂度结构，而生成式解码器则通过学习感知图像块相似度（LPIPS）损失与Wasserstein距离等感知度量来恢复逼真纹理。我们引入了两种互补的实现方案：（i）基于条件Wasserstein生成对抗网络（WGAN）的压缩去噪器，可显式控制率-失真-感知（RDP）权衡；（ii）基于条件扩散的重建策略，通过压缩隐式表示引导迭代去噪过程。我们进一步为加性高斯噪声下基于压缩的最大似然去噪器建立了非渐近理论保证，包括重建误差界与解码错误概率界。在合成噪声与真实噪声基准测试上的实验表明，该方法在保持竞争力失真性能的同时，实现了感知质量的一致性提升。