Super-resolution reconstruction of light field image based on dynamic adjustment of attention mechanism

Abstract

The light field imaging has the problem of mutual restriction between spatial resolution and angular resolution. In this paper, we propose a new blind network based on dynamic adjustment of attention mechanism, and the network can reconstruct the low-resolution images with the multiple blur kernels and noise levels to realize super-resolution reconstruction of the light field images. Firstly, the original sub-aperture images array with low-resolution is used to estimate the blur kernel and noise level as auxiliary information. Then, the parameter features are extracted by the dynamic adjustment module, and the degradation representation is combined with the image features to adapt to various blur kernels and noises. After that, the feature information of the light field image is calculated by the distg-block attention block, and a finer feature map can be obtained for subsequent network learning. Finally, the feature map is up-sampled to obtain a sub-aperture image array. The experimental results show that the proposed method can improve the spatial resolution of the light field images by four times, and obtain clearer light field images with more details.