Efficient Multi-disparity Transformer for Light Field Image Super-resolution
<|reference_start|>Efficient Multi-disparity Transformer for Light Field Image Super-resolution: This paper presents the Multi-scale Disparity Transformer (MDT), a novel Transformer tailored for light field image super-resolution (LFSR) that addresses the issues of computational redundancy and disparity entanglement caused by the indiscriminate processing of sub-aperture images inherent in conventional methods. MDT features a multi-branch structure, with each branch utilising independent disparity self-attention (DSA) to target specific disparity ranges, effectively reducing computational complexity and disentangling disparities. Building on this architecture, we present LF-MDTNet, an efficient LFSR network. Experimental results demonstrate that LF-MDTNet outperforms existing state-of-the-art methods by 0.37 dB and 0.41 dB PSNR at the 2x and 4x scales, achieving superior performance with fewer parameters and higher speed.<|reference_end|>
@article{hu2024efficient,
    title={Efficient Multi-disparity Transformer for Light Field Image
  Super-resolution},
    author={Zeke Zexi Hu, Haodong Chen, Yuk Ying Chung, Xiaoming Chen},
    journal={arXiv preprint arXiv:2407.15329},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.15329},
    primaryClass={eess.IV cs.CV}
}