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Found 4 papers, 4 papers with code
Enhanced Hyperspectral Image Super-Resolution via RGB Fusion and TV-TV Minimization
Such methods, however, cannot guarantee that the input measurements are satisfied in the recovered image, since the learned parameters by the network are applied to every test image.
Overcoming Measurement Inconsistency in Deep Learning for Linear Inverse Problems: Applications in Medical Imaging
We then propose a framework that post-processes the output of DNNs with an optimization algorithm that enforces measurement consistency.
Robust Single-Image Super-Resolution via CNNs and TV-TV Minimization
Single-image super-resolution is the process of increasing the resolution of an image, obtaining a high-resolution (HR) image from a low-resolution (LR) one.
Single Image Super-Resolution via CNN Architectures and TV-TV Minimization
Super-resolution (SR) is a technique that allows increasing the resolution of a given image.
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