Revisiting Large Kernel Convolution for Light Field Image Angular Super-Resolution

Light field (LF) image angular super-resolution (LFASR) aims to reconstruct densely-sampled LF images from a sparsely-sampled sub-aperture image array. However, mainstream LFASR methods based on convolutional neural networks (CNNs) usually suffer from long-range modeling capability due to the limited receptive field of standard convolutions. This leads to insufficient utilization of non-local spatial and angular information under large disparity situations. More recently, some Transformer-based methods have emerged to tackle this issue and achieve promising performance, however, the self-attention mechanism is suboptimal to recover detailed textures and computationally expensively. To tackle these issues, we propose a novel architecture Large Kernel Convolution Attention (LKCA) which combines large kernel convolutions with the transformer architecture to enhance the ability to capture long-range dependencies of LF images. We design a Muti-scale Hybrid Attention Block (MHAB) based on LKCA to fully utilize non-local spatial-angular information to exploit spatial, angular, and epipolar plane image features. Extensive experiments are carried out to show that our method can achieve more accurate reconstruction effects and generate more evident texture compared with the state-of-the-art methods.