The spatial and angular resolution are comparatively low owing to the limitation of the resolution of its pixel sensor which is due to design reasons. To address the above problem, this paper presented a new super-resolution method based on global and local views to simultaneously enhance both the spatial and angular resolutions of a light field image. First of all, it adaptively chose local views according to the position of the novel view, and then used spatial super-resolution convolution neural network to get high resolution sub-images such as global-view and local-view images, subsequently it extracted and fused the depth and color features from the image obtained by backward warping the input views. Then it put the results into the angular super-resolution network which produced a novel view image as the final result. The experimental results demonstrate that the reconstruction effect of this method is superior to the state-of-the-art's. And in peak signal to noise ratio get 3 dB promotion and structural similarity image measure upgrade about 0.02. In addition, it is not only able to improve spatial and angular resolution of multi-view images, but also to maintain edge information of the novel view, thus effectively avoiding the loss of objects and yielding a better reconstruction effect.

[2019-05-05] Printed Article /doc/2017.12.0835/v2