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。

强调有效图像恢复的关键特征

Emphasizing Crucial Features for Efficient Image Restoration

This paper presents a novel monocular depth estimation method, named ECFNet,
for estimating high-quality monocular depth with clear edges and valid overall
structure from a single RGB image. We make a thorough inquiry about the key
factor that affects the edge depth estimation of the MDE networks, and come to
a ratiocination that the edge information itself plays a critical role in
predicting depth details. Driven by this analysis, we propose to explicitly
employ the image edges as input for ECFNet and fuse the initial depths from
different sources to produce the final depth. Specifically, ECFNet first uses a
hybrid edge detection strategy to get the edge map and edge-highlighted image
from the input image, and then leverages a pre-trained MDE network to infer the
initial depths of the aforementioned three images. After that, ECFNet utilizes
a layered fusion module (LFM) to fuse the initial depth, which will be further
updated by a depth consistency module (DCM) to form the final estimation.
Extensive experimental results on public datasets and ablation studies indicate
that our method achieves state-of-the-art performance. Project page:
this https URL

本文提出了名为 ECFNet 的新型单目深度估计方法，通过单个 RGB 图像准确预测高质量的单目深度，应用了图像边缘信息和深度一致性模块来融合不同来源的初始深度，实现了领先的性能。