Abstract
In this paper, we propose an improved generative adversarial network (GAN) for image compression artifacts reduction task (artifacts reduction by GANs, ARGAN). The lossy compression leads to quite complicated compression artifacts, especially blocking artifacts and ringing effects. To handle this problem, we choose generative adversarial networks as an effective solution to reduce diverse compression artifacts. The structure of “U-NET” style is adopted as the generative network in the GAN. A discriminator network is designed in a convolutional manner to differentiate the restored images from the ground truth distribution. This approach can help improve the performance because the adversarial loss aggressively encourages the output image to be close to the distribution of the ground truth. Our method not only learns an end-to-end mapping from input degraded image to corresponding restored image, but also learns a loss function to train this mapping. Benefit from the improved GANs, we can achieve desired results without hand-engineering the loss functions. The experiments show that our method achieves better performance than the state-of-the-art methods.
Highlights
Image restoration technology has become one of the most important applications in computer vision and computer graphics and attracted increasing attention in the field of digital image processing, such as image haze removal [1], image super-resolution [2,3,4], image deblur [5, 6], and image understanding [7]
Image compression artifacts reduction aims at recovering a sharp image from the degraded image which is formed by JPEG compression or other causes
The proposed artifacts reduction by generative adversarial networks (GANs) (ARGAN) consists of two feed-forward convolutional neural networks (CNNs): the generative network G and the discriminative network D
Summary
Image restoration technology has become one of the most important applications in computer vision and computer graphics and attracted increasing attention in the field of digital image processing, such as image haze removal [1], image super-resolution [2,3,4], image deblur [5, 6], and image understanding [7]. Image compression artifacts reduction aims at recovering a sharp image from the degraded image which is formed by JPEG compression or other causes. We use a deep learning-based approach for image compression artifacts reduction. We propose a principled and efficient generative adversarial network (GAN) for this task. We denote the proposed networks as artifacts reduction by GANs (ARGAN) which was inspired from the GANs [8]. Similar to the standard GANs, ARGAN consists of two feed-forward convolutional neural networks (CNNs), the generative network G and the discriminative network D. Our proposed method differs from the existing traditional [9] or other deep learning-based approaches [10]. We are the first to use (GANs) for image compression artifacts reduction
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