Attention in Attention Network for Image Super Resolution (A2N) A2N model pre trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper Attention in Attention Network for Image Super Resolution by Chen et al. (2021) and first released in this repository. The goal of image super resolution is to restore a high resolution (HR) image from a single low resolution (LR) image. The image below shows the ground truth (HR), the bicubic upscaling x2 and model upscaling x2. Model description The A2N model proposes an attention in attention network (A2N) for highly accurate image SR. Specifically, the A2N consists of a non attention branch and a coupling attention branch. Attention dropout module is proposed to generate dynamic attention weights for these two branches based on input features that can suppress unwanted attention adjustments. This allows attention modules to specialize to beneficial examples without otherwise penalties and thus greatly improve the capacity of the attention network with little parameter overhead. More importantly the model is lightweight and fast to train (~1.5m…
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