  Image restoration aims to enhance low quality images, producing high quality
images that exhibit natural visual characteristics and fine semantic
attributes. Recently, the diffusion model has emerged as a powerful technique
for image generation, and it has been explicitly employed as a backbone in
image restoration tasks, yielding excellent results. However, it suffers from
the drawbacks of slow inference speed and large model parameters due to its
intrinsic characteristics. In this paper, we introduce a new perspective that
implicitly leverages the diffusion model to assist the training of image
restoration network, called DiffLoss, which drives the restoration results to
be optimized for naturalness and semantic-aware visual effect. To achieve this,
we utilize the mode coverage capability of the diffusion model to approximate
the distribution of natural images and explore its ability to capture image
semantic attributes. On the one hand, we extract intermediate noise to leverage
its modeling capability of the distribution of natural images, which serves as
a naturalness-oriented optimization space. On the other hand, we utilize the
bottleneck features of diffusion model to harness its semantic attributes
serving as a constraint on semantic level. By combining these two designs, the
overall loss function is able to improve the perceptual quality of image
restoration, resulting in visually pleasing and semantically enhanced outcomes.
To validate the effectiveness of our method, we conduct experiments on various
common image restoration tasks and benchmarks. Extensive experimental results
demonstrate that our approach enhances the visual quality and semantic
perception of the restoration network.
