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Image super resolution


Jiahao Li

12/09/2023

Supervised by Xianfang Sun; Moderated by Padraig Corcoran

Image Super-Resolution (SR) is a fundamental class of image processing techniques in computer vision to recover a HR image from the LR one, which improves visual per- ception and enhance details of the image. Deep-learning-based methods have shown impressive performance in SR tasks. However, the most previous SISR methods only treat SR of different scale factors as independent tasks. They train a specific model for each scale factor which is inefficient in computing. This work proposes the Hybrid Attention Transformer based Neural Operator in Im- age Super-resolution (HAT-SRNO), a deep network that aims to detail restoration task at arbitray upsampling scale. First, this work simply divides the SR model into fea- ture extracting parts and upsampling parts. Then, The feature extracting module, Hy- brid Attention Transformer (HAT) activates more pixels for reconstruction by combin- ing channel attention and self-attention. At the meantime, treating the LR-HR image pairs as continuous functions approximated with different grid sizes, Super-resolution Neural Operator (SRNO) can learn the map between different levels of discretisation of continuous functions. Experiments that HAT-SRNO attains better performance over upsampling scale x2 in SET5 and DIV2K dataset on quantiative evaluation and produces more nature images with repeated similar pattern patches on visual evaluation.


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