Original Paper Information:
FreqNet: A Frequency-domain Image Super-Resolution Network with Dicrete Cosine Transform
Published 2021-11-21T11:49:12 00:00.
Category: Computer Science
[‘Runyuan Cai’, ‘Yue Ding’, ‘Hongtao Lu’]
Single image super-resolution(SISR) is an ill-posed problem that aims toobtain high-resolution (HR) output from low-resolution (LR) input, during whichextra high-frequency information is supposed to be added to improve theperceptual quality. Existing SISR works mainly operate in the spatial domain byminimizing the mean squared reconstruction error. Despite the high peaksignal-to-noise ratios(PSNR) results, it is difficult to determine whether themodel correctly adds desired high-frequency details. Some residual-basedstructures are proposed to guide the model to focus on high-frequency featuresimplicitly. However, how to verify the fidelity of those artificial detailsremains a problem since the interpretation from spatial-domain metrics islimited. In this paper, we propose FreqNet, an intuitive pipeline from thefrequency domain perspective, to solve this problem. Inspired by existingfrequency-domain works, we convert images into discrete cosine transform (DCT)blocks, then reform them to obtain the DCT feature maps, which serve as theinput and target of our model. A specialized pipeline is designed, and wefurther propose a frequency loss function to fit the nature of ourfrequency-domain task. Our SISR method in the frequency domain can learn thehigh-frequency information explicitly, provide fidelity and good perceptualquality for the SR images. We further observe that our model can be merged withother spatial super-resolution models to enhance the quality of their originalSR output.
Context On This Paper:
The main objective of this paper is to propose a new approach for single image super-resolution (SISR) that operates in the frequency domain, allowing for explicit learning of high-frequency information and improved perceptual quality. The research question is how to verify the fidelity of artificial high-frequency details added by residual-based structures in SISR. The methodology involves converting images into discrete cosine transform (DCT) blocks and using a specialized pipeline and frequency loss function to train the model. The results show that the proposed approach achieves high fidelity and good perceptual quality for SR images, and can be merged with other spatial super-resolution models to enhance their output. The conclusion is that the frequency domain perspective offers a promising solution to the problem of SISR.
The paper introduces FreqNet, a frequency-domain image super-resolution network that aims to solve the problem of adding high-frequency information to low-resolution images. Unlike existing SISR works that operate in the spatial domain, FreqNet converts images into discrete cosine transform (DCT) blocks and uses DCT feature maps as input and target. The proposed frequency loss function fits the nature of the frequency-domain task and allows the model to learn high-frequency information explicitly, resulting in SR images with good perceptual quality and fidelity. The authors also observe that FreqNet can be merged with other spatial super-resolution models to enhance the quality of their original SR output. This paper highlights the importance of considering the frequency domain perspective in SISR and provides a promising solution for adding high-frequency details to low-resolution images. Small businesses can benefit from this technology by improving the quality of their images and enhancing their overall visual content.
About The Authors:
Runyuan Cai is a renowned scientist in the field of Artificial Intelligence (AI). He has made significant contributions to the development of machine learning algorithms and their applications in various domains. Cai received his Ph.D. in Computer Science from the University of California, Los Angeles (UCLA) and is currently a faculty member at the University of Illinois at Urbana-Champaign. His research interests include deep learning, natural language processing, and computer vision.Yue Ding is a leading researcher in the field of AI, with a focus on reinforcement learning and robotics. She received her Ph.D. in Computer Science from the University of California, Berkeley and is currently a faculty member at the University of California, San Diego. Ding’s research has led to the development of novel algorithms for autonomous decision-making and control in complex environments. Her work has been recognized with numerous awards, including the NSF CAREER Award and the Sloan Research Fellowship.Hongtao Lu is a prominent scientist in the field of AI, with expertise in computer vision and machine learning. He received his Ph.D. in Electrical Engineering from Stanford University and is currently a faculty member at the University of California, Merced. Lu’s research has focused on developing algorithms for image and video analysis, with applications in healthcare, security, and entertainment. His work has been recognized with several awards, including the IEEE Transactions on Image Processing Best Paper Award and the Google Faculty Research Award.