  Image deblurring continues to achieve impressive performance with the
development of generative models. Nonetheless, there still remains a
displeasing problem if one wants to improve perceptual quality and quantitative
scores of recovered image at the same time. In this study, drawing inspiration
from the research of transformer properties, we introduce the pretrained
transformers to address this problem. In particular, we leverage deep features
extracted from a pretrained vision transformer (ViT) to encourage recovered
images to be sharp without sacrificing the performance measured by the
quantitative metrics. The pretrained transformer can capture the global
topological relations (i.e., self-similarity) of image, and we observe that the
captured topological relations about the sharp image will change when blur
occurs. By comparing the transformer features between recovered image and
target one, the pretrained transformer provides high-resolution blur-sensitive
semantic information, which is critical in measuring the sharpness of the
deblurred image. On the basis of the advantages, we present two types of novel
perceptual losses to guide image deblurring. One regards the features as
vectors and computes the discrepancy between representations extracted from
recovered image and target one in Euclidean space. The other type considers the
features extracted from an image as a distribution and compares the
distribution discrepancy between recovered image and target one. We demonstrate
the effectiveness of transformer properties in improving the perceptual quality
while not sacrificing the quantitative scores (PSNR) over the most competitive
models, such as Uformer, Restormer, and NAFNet, on defocus deblurring and
motion deblurring tasks.
