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Subspace Constraint for Single Image Super-Resolution
2021Recently, single image super-resolution (SISR) algorithms based on convolutional neural networks (CNN) have proliferated and achieved significant success. However, most of them use the same constraint to both low-frequency and high-frequency features in the loss function.
Yanlin Zhang, Ding Qin, Xiaodong Gu 0001
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Single image super resolution for license plate
2010 Sixth International Conference on Natural Computation, 2010A single image super resolution algorithm for license plate preprocessing is proposed in this paper. The image to be enhanced is modeled as a Markov Random Field and is estimated from the input low resolution image by image patch pairs. From the input image and the training set, observation function and compatibility function can be calculated.
Yanbing Xue +3 more
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Single image super-resolution in frequency domain
2012 IEEE Southwest Symposium on Image Analysis and Interpretation, 2012This paper presents a neighborhood dependent components based feature learning (NDCFL) for regression analysis in single image super-resolution. Given a low resolution input, the method uses directional Fourier phase feature components to adaptively learn the regression kernel based on local covariance to estimate the high resolution image.
Mohammad Moinul Islam +3 more
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Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal, 2019
Super-Resolution (SR) of a single image is a classic problem in computer vision. The goal of image super-resolution is to produce a high-resolution image from a low-resolution image. This paper presents a popular model, super-resolution convolutional neural network (SRCNN), to solve this problem. This paper also examines an improvement to SRCNN using a
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Super-Resolution (SR) of a single image is a classic problem in computer vision. The goal of image super-resolution is to produce a high-resolution image from a low-resolution image. This paper presents a popular model, super-resolution convolutional neural network (SRCNN), to solve this problem. This paper also examines an improvement to SRCNN using a
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Non-parametric single image super resolution
The 19th Korea-Japan Joint Workshop on Frontiers of Computer Vision, 2013In this paper, we introduce a single image super resolution based on non-parametric local information. The basic idea of the proposed method is to use a property, which is inferred by relations between input and its lower resolution images, of an unknown high resolution image.
Yunsang Han +2 more
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Edge-preserving single image super-resolution
Proceedings of the 19th ACM international conference on Multimedia, 2011This paper proposes a novel approach to single image super-resolution. First, an image up-sampling scheme is proposed which takes the advantages of both bilateral filtering and mean shift image segmentation. Then we use a shock filter to enhance strong edges in the initial up-sampling result and obtain an intermediate high-resolution image. Finally, we
Qiang Zhou +3 more
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Gradient boosting for single image super-resolution
Information Sciences, 2018Abstract The learning-based single image super-resolution (SISR) algorithm aims at recovering a high-resolution (HR) image from low-resolution (LR) input. The quality of the HR output mainly depends on the strength of the learning algorithms. Observing that gradient boosting is powerful in dealing with learning problems, we propose a new SISR ...
Dongping Xiong +3 more
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Edge-Guided Single Depth Image Super Resolution
IEEE Transactions on Image Processing, 2014Recently, consumer depth cameras have gained significant popularity due to their affordable cost. However, the limited resolution and the quality of the depth map generated by these cameras are still problematic for several applications. In this paper, a novel framework for the single depth image superresolution is proposed.
Jun Xie +2 more
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Single Image Super-Resolution for SAR Images
2021Single image Super-Resolution (SR) is a method to get a high-resolution image out of a single Low-Resolution (LR) image. SR is used in different domains, such as medical imaging, satellite imaging, and security imaging. Using SR compared to LR images speeds up training convergence and boosts recognition and segmentation accuracy.
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Learn to Zoom in Single Image Super-Resolution
IEEE Signal Processing Letters, 2022Zili Zhang 0002 +3 more
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