CLBSR: A deep curriculum learning-based blind image super resolution network using geometrical prior

IF 4.2 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Alireza Esmaeilzehi , Amir Mohammad Babaei , Farshid Nooshi , Hossein Zaredar , M. Omair Ahmad
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引用次数: 0

Abstract

Blind image super resolution (SR) is a challenging computer vision task, which involves enhancing the quality of the low-resolution (LR) images obtained by various degradation operations. Deep neural networks have provided state-of-the-art performances for the task of image SR in a blind fashion. It has been shown in the literature that by decoupling the task of blind image SR into the blurring kernel estimation and high-quality image reconstruction, superior performance can be obtained. In this paper, we first propose a novel optimization problem that, by using the geometrical information as prior, is able to estimate the blurring kernels in an accurate manner. We then propose a novel blind image SR network that employs the blurring kernel thus estimated in its network architecture and learning algorithm in order to generate high-quality images. In this regard, we utilize the curriculum learning strategy, wherein the training process of the SR network is initially facilitated by using the ground truth (GT) blurring kernel and then continued with the estimated blurring kernel obtained from our optimization problem. The results of various experiments show the effectiveness of the proposed blind image SR scheme in comparison to state-of-the-art methods on various degradation operations and benchmark datasets.
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来源期刊
Image and Vision Computing
Image and Vision Computing 工程技术-工程:电子与电气
CiteScore
8.50
自引率
8.50%
发文量
143
审稿时长
7.8 months
期刊介绍: Image and Vision Computing has as a primary aim the provision of an effective medium of interchange for the results of high quality theoretical and applied research fundamental to all aspects of image interpretation and computer vision. The journal publishes work that proposes new image interpretation and computer vision methodology or addresses the application of such methods to real world scenes. It seeks to strengthen a deeper understanding in the discipline by encouraging the quantitative comparison and performance evaluation of the proposed methodology. The coverage includes: image interpretation, scene modelling, object recognition and tracking, shape analysis, monitoring and surveillance, active vision and robotic systems, SLAM, biologically-inspired computer vision, motion analysis, stereo vision, document image understanding, character and handwritten text recognition, face and gesture recognition, biometrics, vision-based human-computer interaction, human activity and behavior understanding, data fusion from multiple sensor inputs, image databases.
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