Ultra-high-definition (UHD) image restoration faces significant challenges due to its high resolution, complex content, and intricate details. To cope with these challenges, we analyze the restoration process in depth through a progressive spectral perspective, and deconstruct the complex UHD restoration problem into three progressive stages: zero-frequency enhancement, low-frequency restoration, and high-frequency refinement. Building on this insight, we propose a novel framework, ERR, which comprises three collaborative sub-networks: the zero-frequency enhancer (ZFE), the low-frequency restorer (LFR), and the high-frequency refiner (HFR). Specifically, the ZFE integrates global priors to learn global mapping, while the LFR restores low-frequency information, emphasizing reconstruction of coarse-grained content. Finally, the HFR employs our designed frequency-windowed kolmogorov-arnold networks (FW-KAN) to refine textures and details, producing high-quality image restoration. Our approach significantly outperforms previous UHD methods across various tasks, with extensive ablation studies validating the effectiveness of each component. The code is available at here.

The performance of image restoration (IR) is highly de-pendent on the reconstruction quality of diverse contents with varying complexity. However, most IR approaches model the mapping between various complexity contents of inputs and outputs through the repeated feature calcu-lation propagation mechanism in a unified pipeline, which leads to unsatisfactory results. To address this issue, we propose an explicit Content Decoupling framework for IR, dubbed CoDe, to end-to-end model the restoration process by utilizing decoupled content components in a divide-and- conquer-like architecture. Specifically, a Content Decou-pling Module is first designed to decouple content components of inputs and outputs according to thefrequency spec-tra adaptively generatedfrom the transform domain. In ad-dition, in order to harness the divide-and-conquer strategy for reconstructing decoupled content components, we pro-pose an IR Network Container. It contains an optimized version, which is a streamlining of an arbitrary IR net-work, comprising the cascaded modulated subnets and a Reconstruction Layers Pool. Finally, a Content Consistency Loss is designed from the transform domain perspective to supervise the restoration process of each content component and further guide the feature fusion process. Exten-sive experiments on several IR tasks, such as image super-resolution, image denoising, and image blurring, covering both real and synthetic settings, demonstrate that the pro-posed paradigm can effectively take the performance of the original network to a new state-of-the-art level in multiple benchmark datasets (e.g., O.34dB@Set5 × 4 over DAT).