A novel refocusing distance measurement method using super‐resolved light field images

Abstract

This paper proposes a novel distance measurement method using superresolved light-field images. We first superresolve the light field using the hybrid cross-resolution input based on PatchMatch and convolutional neural network method, which combines and takes advantage of these two methods. In this way, we can explore the similarity between images and deal with challenging scenes effectively. The scaling factor is up to eight times, which is much larger than the ordinary superresolution scaling factor, and our superresolution method can also maintain a satisfactory accuracy. With the traditional idea of focus ranging, the light-field 3D measurement method is established. A proper definition evaluation function suitable for light-field data is selected to evaluate the imaging quality, and the triangle barycenter interpolation method is proposed to measure 3D scenes. Experimental results demonstrate that the proposed method not only improves the quality of the reconstructed high-resolution light field but also has the ability of the distance measurement. It indicates that the light-field imaging is a promising 3D measurement technique.