GAN-Based Image Deblurring Using DCT Loss With Customized Datasets

• Published in: IEEE access Vol. 9; pp. 135224 - 135233
• Main Authors: Tomosada, Hiroki, Kudo, Takahiro, Fujisawa, Takanori, Ikehara, Masaaki
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; blind deconvolution; Blocking; Computing time; Customization; Datasets; Deconvolution; Discrete cosine transform; discrete cosine transform (DCT); Discrete cosine transforms; GAN; Generative adversarial networks; Generators; Image deblurring; Image quality; Image restoration; Noise measurement; Noise reduction; non-uniform; Optical disks; Training
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3116194

In this paper, we propose a high quality image deblurring method that uses discrete cosine transform (DCT) and requires less computational complexity. We train our model on a new dataset which is customized to include images with large motion blurs. Recently, Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN) based algorithms have been proposed for image deblurring. Moreover, multi-scale and multi-patch architectures of CNN restore blurred images clearly and suppress more ringing or blocking artifacts, but they take a longer time to process. To improve the quality of deblured images and reduce the computational time, we propose a method called "DeblurDCTGAN" that preserves texture and suppresses ringing artifacts in the restored image without multi-scale or multi-patch architecture using DCT based loss. This loss compares the restored image and the ground truth image in the frequency domain. With this loss, DeblurDCTGAN can reduce block noise and ringing artifacts while maintaining deblurring performance. Our experimental results show that DeblurDCTGAN gets the highest performances in terms of PSNR, SSIM, and running time compared with conventional methods. In terms of real image datasets, DeblurDCTGAN shows a better performance by using a customized training dataset made from GoPro, DVD, NFS and HIDE training datasets. Experimented code with pre-trained weights, datasets and results are available at https://github.com/Hiroki-Tomosada/DCTGAN-master