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Medical image compression using class-wise superresolution.
We present a new method for compression of images based on class-wise superresolution for the problem of the lossless decomposition of a multi-class image into component images of the corresponding classes. This new method leads to high quality images for both source images and recovered ones, compared to competing methods. In order to maintain the low complexity achieved by using an implementation of the discrete cosine transform on each resolution scale, we use FFT as ec5d62056f lisces
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