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- W2891660938 abstract "Learning based single image super resolution (SISR) methods have achieved notable results, however, they require large datasets for training, and may struggle when there is a mismatch between the testing and training data. To overcome these drawbacks, we propose an approach, named U - FRESH, which only requires a small dataset but can achieve state-of-the-art performance also in the presence of training and testing mismatches. We accomplish this by leveraging a method called FRESH, which enhances the image resolution using FRI theory. We start upscaling from the FRESH generated low resolution image. To minimize the reconstruction error, we propose a new regression selection technique to make the mapping more reliable and robust, and a wavelet based back projection technique to improve the quality of the reconstructed image. Based on U - FRESH, we also propose a new framework based on JPEG 2000 for image compression. Numerical results show that our U-FRESH method achieves state-of-the-art performance in SISR and provides better compression results than JPEG 2000." @default.
- W2891660938 created "2018-09-27" @default.
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- W2891660938 date "2018-04-01" @default.
- W2891660938 modified "2023-09-24" @default.
- W2891660938 title "U-Fresh: An Fri-Based Single Image Super Resolution Algorithm and An Application in Image Compression" @default.
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- W2891660938 doi "https://doi.org/10.1109/icassp.2018.8461383" @default.
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