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Deep learning based super resolution, without using a GAN.

Super-resolution is a technique that enhances the quality and detail of low-resolution images, effectively transforming them into higher-resolution versions. Traditional upscaling methods often result in images lacking fine details and may introduce defects or compression artifacts. Deep learning approaches, particularly those utilizing Generative Adversarial Networks (GANs), have shown significant improvements in this area. However, training GANs can be complex and resource-intensive.

In this article, we explore an alternative deep learning method for super-resolution that does not rely on GANs. By leveraging techniques from the Fastai library and course, we train a model capable of enhancing image resolution effectively. This approach simplifies the training process while still delivering impressive results in image restoration and inpainting. The methodology combines various advanced techniques, some of which are relatively unique in their application as of early 2019.

The practical benefits of this GAN-free super-resolution method are substantial. It enables the recovery of high-quality images from low-resolution inputs, which is invaluable in fields such as medical imaging, where enhanced image clarity can be life-saving. Additionally, it offers potential for efficient data transmission by allowing the transfer of lower-resolution images that can be upscaled upon receipt, optimizing bandwidth usage. This method provides a more accessible and less resource-demanding solution for image enhancement tasks.

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Deep learning based super resolution, without using a GAN