Image restoration is a challenging ill-posed problem which estimates latent
sharp image from its degraded counterpart. Although the existing methods have
achieved promising performance by designing novelty architecture of module,
they ignore the fact that different regions in a corrupted image undergo
varying degrees of degradation. In this paper, we propose an efficient and
effective framework to adapt to varying degrees of degradation across different
regions for image restoration. Specifically, we design a spatial and frequency
attention mechanism (SFAM) to emphasize crucial features for restoration. SFAM
consists of two modules: the spatial domain attention module (SDAM) and the
frequency domain attention module (FDAM). The SFAM discerns the degradation
location through spatial selective attention and channel selective attention in
the spatial domain, while the FDAM enhances high-frequency signals to amplify
the disparities between sharp and degraded image pairs in the spectral domain.
Additionally, to capture global range information, we introduce a multi-scale
block (MSBlock) that consists of three scale branches, each containing multiple
simplified channel attention blocks (SCABlocks) and a multi-scale feed-forward
block (MSFBlock). Finally, we propose our ECFNet, which integrates the
aforementioned components into a U-shaped backbone for recovering high-quality
images. Extensive experimental results demonstrate the effectiveness of ECFNet,
outperforming state-of-the-art (SOTA) methods on both synthetic and real-world
datasets.

本研究提出了一种高效有效的框架，用于适应不同区域间存在的不同程度退化的图像恢复，并且在合成和真实数据集上优于目前的最先进方法。