Example-Based Super-Resolution
IEEE Computer Graphics and Applications
Limits on Super-Resolution and How to Break Them
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Adaptive license plate image extraction
CompSysTech '04 Proceedings of the 5th international conference on Computer systems and technologies
Extension of phase correlation to subpixel registration
IEEE Transactions on Image Processing
Fast and robust multiframe super resolution
IEEE Transactions on Image Processing
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This paper presents a high-resolution image reconstruction method from low-resolution image sequence. It is difficult to recognize details from a low-resolution image because of severe aliasing and poor image quality, hence recognition from the low-resolution image may result in false recognition decision. In order to improve the recognition performance, the proposed method performs a reconstruction-based super-resolution technique as a preprocessing. Then, we adopt a learning-based superresolution technique to make high-resolution images. The proposed method also considers the illumination change between an input image and training images. To verify the accuracy and reliability of the proposed method, experiments and numerical analyses were performed with several video sequences of a moving car that simulate real surveillance systems.