  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 coefficient 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.
