  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.
