GAN-Based Image Deblurring Using DCT Discriminator
Hiroki Tomosada, Takahiro Kudo, Takanori Fujisawa, Masaaki Ikehara · 2021
In this paper, we propose high quality image deblurring by using discrete cosine transform (DCT) with less computational complexity. Recently, Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN) based algorithms have been proposed for image deblurring. Moreover, multi-scale architecture of CNN restores blurred image cleary and suppresses more ringing artifacts or block noise, but it takes much time to process. To solve these problems, we propose a method that preserves texture and suppresses ringing artifacts in the restored image without multi-scale architecture using DCT based loss named “DeblurDCTGAN.”. It compares frequency domain of the images made from deblurred image and ground truth image by using DCT. Hereby, DeblurDCTGAN can reduce block noise or ringing artifacts while maintaining deblurring performance. Our experimental results show that DeblurDCTGAN gets the highest performances on both PSNR and SSIM comparing with other conventional methods in GoPro, DVD, NFS and HIDE test Dataset. Also, the running time per pair of DeblurDCTGAN is faster than others.