ClassSR efficiently utilizes the available computational resources to decompose original image, super-resolve and restore it in SR networks.
Super-Resolution is the process of generating a high-resolution image out of a low-resolution input image. Large-sized low-resolution images are usually broken down into small patches and restored back to high-resolution images. Based on the decomposed images’ complexity (patches of the original image), the super-resolution network takes different computation time. A smooth patch with minimal variations such as sky may take less time to super-resolved, whereas a feature-rich patch with a high-degree of variations such as flowers or butterflies may take more time to super-resolve.
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