Efficient Multi-disparity Transformer for Light Field Image Super-resolution

• URL: http://arxiv.org/abs/2407.15329v1
• Date: Mon, 22 Jul 2024 02:23:09 GMT
• Title: Efficient Multi-disparity Transformer for Light Field Image Super-resolution
• Authors: Zeke Zexi Hu, Haodong Chen, Yuk Ying Chung, Xiaoming Chen,
• Abstract summary: This paper presents the Multi-scale Disparity Transformer (MDT), a novel Transformer tailored for light field image super-resolution (LFSR) MDT addresses the issues of computational redundancy and disparity entanglement caused by the indiscriminate processing of sub-aperture images. Building on this architecture, we present LF-MDTNet, an efficient LFSR network.
• Score: 6.814658355110824
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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