Xpixelgroup

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hat

xpixelgroup

Total Score

24

HAT is an image super-resolution transformer model developed by researchers at xpixelgroup. It builds upon previous transformer-based approaches for super-resolution, such as SwinIR, by "activating more pixels" to improve performance on benchmark datasets like Set5, Set14, and Urban100. HAT also includes a smaller variant, HAT-S, which has a lower parameter count and computational cost while still achieving strong results. Model inputs and outputs HAT takes a low-resolution image as input and outputs a higher-resolution version of that image. The input image can be of any size, and the model will upscale it by a factor of 4x. Inputs Image**: The low-resolution input image. Outputs Image**: The super-resolved, high-resolution output image. Capabilities HAT demonstrates state-of-the-art performance on standard image super-resolution benchmarks, outperforming previous transformer-based models like SwinIR in terms of PSNR and SSIM metrics. The model is also able to handle a variety of image types, from natural scenes to manga and anime-style artwork. What can I use it for? HAT can be used for a variety of image enhancement and restoration tasks, such as upscaling low-resolution images for use in print, video, or digital media. It could be particularly useful for applications where high-quality images are required, such as medical imaging, satellite and aerial photography, or visual effects in filmmaking. The model's strong performance on diverse image types also makes it suitable for tasks like enhancing manga, anime, and other stylized artwork. Things to try One interesting aspect of HAT is its ability to handle images of varying sizes without requiring any pre-processing or resizing. This makes it easy to apply the model to real-world scenarios where the input image dimensions may be irregular or unknown. Developers could experiment with integrating HAT into their image processing pipelines to seamlessly upscale and enhance images as needed. Additionally, the availability of both the larger HAT model and the more compact HAT-S variant allows users to choose the right balance of performance and resource usage for their specific application requirements.

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Updated 5/19/2024