Abstract
In this paper, we propose a method to solve the image
restoration problem, which tries to restore the details of a
corrupted image, especially due to the loss caused by JPEG
compression. We have treated an image in the frequency
domain to explicitly restore the frequency components lost
during image compression. In doing so, the distribution in
the frequency domain is learned using the cross entropy
loss. Unlike recent approaches, we have reconstructed the
details of an image without using the scheme of adversarial
training. Rather, the image restoration problem is treated as
a classification problem to determine the frequency coeffi-
cient for each frequency band in an image patch. In this paper, we show that the proposed method effectively restores a
JPEG-compressed image with more detailed high frequency
components, making the restored image more vivid