Image restoration has made marvelous progress with the advent of deep
learning. Previous methods usually rely on designing powerful network
architecture to elevate performance, however, the natural visual effect of the
restored results is limited by color and texture distortions. Besides the
visual perceptual quality, the semantic perception recovery is an important but
often overlooked perspective of restored image, which is crucial for the
deployment in high-level tasks. In this paper, we propose a new perspective to
resort these issues by introducing a naturalness-oriented and semantic-aware
optimization mechanism, dubbed DiffLoss. Specifically, inspired by the powerful
distribution coverage capability of the diffusion model for natural image
generation, we exploit the Markov chain sampling property of diffusion model
and project the restored results of existing networks into the sampling space.
Besides, we reveal that the bottleneck feature of diffusion models, also dubbed
h-space feature, is a natural high-level semantic space. We delve into this
property and propose a semantic-aware loss to further unlock its potential of
semantic perception recovery, which paves the way to connect image restoration
task and downstream high-level recognition task. With these two strategies, the
DiffLoss can endow existing restoration methods with both more natural and
semantic-aware results. We verify the effectiveness of our method on
substantial common image restoration tasks and benchmarks. Code will be
available at this https URL

通过引入自然性导向和语义感知优化机制 DiffLoss，本文旨在改善图像恢复领域中的颜色和纹理失真问题，并提高图像的语义感知恢复能力，以实现图像恢复任务和高级识别任务的连接。