Abstract
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.
Abstract (translated)
图像压缩和去噪在图像处理中是一个基本挑战，许多实际应用都依赖于它。为了解决实际需求，当前的解决方案可以分为两个主要策略：1）序列方法；2）联合方法。然而，序列方法的一个缺点是，多个独立模型的信息损失会导致错误累积。最近，学术界开始尝试通过端到端的联合方法来解决这个问题。大多数方法忽略了噪声图像不同区域具有不同的特征。为了解决这些问题，本文提出的信号噪声比（SNR）感知联合解决方案同时利用了图像压缩和去噪的局部和非局部特征。我们设计了一个端到端的可训练网络，包括主要编码分支、指导分支和信号噪声比（SNR）感知分支。我们在合成和真实世界数据集上进行了广泛的实验，证明了我们的联合解决方案超越了现有最先进的方法。
URL
https://arxiv.org/abs/2403.14135
