Abstract
Deep unfolding networks (DUNs), combining conventional iterative optimization algorithms and deep neural networks into a multi-stage framework, have achieved remarkable accomplishments in Image Restoration (IR), such as spectral imaging reconstruction, compressive sensing and this http URL unfolds the iterative optimization steps into a stack of sequentially linked this http URL block consists of a Gradient Descent Module (GDM) and a Proximal Mapping Module (PMM) which is equivalent to a denoiser from a Bayesian perspective, operating on Gaussian noise with a known this http URL, existing DUNs suffer from two critical limitations: (i) their PMMs share identical architectures and denoising objectives across stages, ignoring the need for stage-specific adaptation to varying noise levels; and (ii) their chain of structurally repetitive blocks results in severe parameter redundancy and high memory consumption, hindering deployment in large-scale or resource-constrained this http URL address these challenges, we introduce generalized Deep Low-rank Adaptation (LoRA) Unfolding Networks for image restoration, named LoRun, harmonizing denoising objectives and adapting different denoising levels between stages with compressed memory usage for more efficient this http URL introduces a novel paradigm where a single pretrained base denoiser is shared across all stages, while lightweight, stage-specific LoRA adapters are injected into the PMMs to dynamically modulate denoising behavior according to the noise level at each unfolding this http URL design decouples the core restoration capability from task-specific adaptation, enabling precise control over denoising intensity without duplicating full network parameters and achieving up to $N$ times parameter reduction for an $N$-stage DUN with on-par or better this http URL experiments conducted on three IR tasks validate the efficiency of our method.
Abstract (translated)
结合传统迭代优化算法和深度神经网络的深层展开网络（DUNs）在图像恢复领域，如光谱成像重建、压缩感知等任务中取得了显著成就。这类方法通过将迭代步骤拆解为一系列顺序连接的模块化块来工作，每个块包含一个梯度下降模块（GDM）和一个临近映射模块（PMM），后者从贝叶斯视角来看相当于一种去噪器，针对已知高斯噪声进行操作。然而，现有的DUNs面临两个关键限制：(i)它们的PMM在各阶段共享相同的架构及去噪目标，忽视了根据不同阶段所需的特定适应性调整；(ii)重复结构导致严重的参数冗余和高内存消耗，阻碍其在大规模或资源受限环境中的部署。 为解决这些问题，我们提出了广义深度低秩适配（LoRA）展开网络用于图像恢复，命名为LoRun。该方法通过压缩的内存使用量实现了不同阶段去噪目标之间的协调及适应不同的噪声水平，并且提高了效率。这种方法引入了一种新颖的方法：在整个过程中共享一个预训练的基本去噪器，同时在每个PMM中插入轻量级、阶段特定的LoRA适配器来根据展开步骤中的噪声级别动态调节去噪行为。 该设计解耦了核心恢复能力与任务特定适应性调整，从而能够在不复制完整网络参数的情况下实现对去噪强度的精确控制，并且最多可以将N个阶段DUN的参数减少到原来的1/N倍，在保持性能不变或更好的情况下。通过在三个图像恢复任务上的实验验证了我们方法的有效性。
URL
https://arxiv.org/abs/2602.18697
