Digital Picture Processing
Example-Based Super-Resolution
IEEE Computer Graphics and Applications
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Fundamental Limits of Reconstruction-Based Superresolution Algorithms under Local Translation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vehicle Recognition Using Curvelet Transform and SVM
ITNG '07 Proceedings of the International Conference on Information Technology
Example-based single document image super-resolution: a global MAP approach with outlier rejection
Multidimensional Systems and Signal Processing
Efficient implementation of image interpolation as an inverse problem
Digital Signal Processing
A discontinuity adaptive method for super-resolution of license plates
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
Simultaneous iterative image restoration and evaluation of theregularization parameter
IEEE Transactions on Signal Processing
Automatic license plate recognition
IEEE Transactions on Intelligent Transportation Systems
An edge-preserving image interpolation system for a digital camcorder
IEEE Transactions on Consumer Electronics
Warped distance for space-variant linear image interpolation
IEEE Transactions on Image Processing
New edge-directed interpolation
IEEE Transactions on Image Processing
Linear interpolation revitalized
IEEE Transactions on Image Processing
Fast and robust multiframe super resolution
IEEE Transactions on Image Processing
A Nonlinear Least Square Technique for Simultaneous Image Registration and Super-Resolution
IEEE Transactions on Image Processing
Interactive multi-frame reconstruction for mobile devices
Multimedia Tools and Applications
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This paper proposes a new algorithm to perform single-frame image super-resolution (SR) of vehicle license plate (VLP) using soft learning prior. Conventional single-frame SR/interpolation methods such as bi-cubic interpolation often experience over-smoothing near the edges and textured regions. Therefore, learning-based methods have been proposed to handle these shortcomings by incorporating a learning term so that the reconstructed high-resolution images can be guided towards these models. However, existing learning-based methods employ a binary hard-decision approach to determine whether the prior models are fully relevant or totally irrelevant. This approach, however, is inconsistent with many practical applications as the degree of relevance for the prior models may vary. In view of this, this paper proposes a new framework that adopts a soft learning approach in license plate super-resolution. The method integrates image SR with optical character recognition (OCR) to perform VLP SR. The importance of the prior models is estimated through relevance scores obtained from the OCR. These are then incorporated as a soft learning term into a new regularized cost function. Experimental results show that the proposed method is effective in handling license plate SR in both simulated and real experiments.