Image compression and denoising represent fundamental challenges in image processing with many real-world applications. To address practical demands, current solutions can be categorized into two main strategies: 1) sequential method; and 2) joint method. However, sequential methods have the disadvantage of error accumulation as there is information loss between multiple individual models. Recently, the academic community began to make some attempts to tackle this problem through end-to-end joint methods. Most of them ignore that different regions of noisy images have different characteristics. To solve these problems, in this paper, our proposed signal-to-noise ratio~(SNR) aware joint solution exploits local and non-local features for image compression and denoising simultaneously. We design an end-to-end trainable network, which includes the main encoder branch, the guidance branch, and the signal-to-noise ratio~(SNR) aware branch. We conducted extensive experiments on both synthetic and real-world datasets, demonstrating that our joint solution outperforms existing state-of-the-art methods.

翻译：图像压缩与去噪是图像处理中的基本挑战，具有众多实际应用。为满足实际需求，现有解决方案主要分为两类策略：1) 顺序方法；2) 联合方法。然而，顺序方法存在误差累积的缺陷，因为多个独立模型之间存在信息损失。近期，学术界开始尝试通过端到端联合方法解决这一问题。多数方法忽略了噪声图像的不同区域具有不同特性。为解决这些问题，本文提出的信噪比感知联合方案，同时利用局部与非局部特征实现图像压缩与去噪。我们设计了一个端到端可训练网络，包含主编码器分支、引导分支和信噪比感知分支。在合成数据集和真实数据集上的大量实验表明，我们的联合方案优于现有最先进方法。