Exposure Interpolation Via Hybrid Learning

Abstract

Deep learning based methods have become dominant solutions to many image processing problems. A natural question would be "Is there any space for conventional methods on these problems?" In this paper, exposure interpolation is taken as an example to answer this question and the answer is "Yes". A new hybrid learning framework is introduced to interpolate a medium exposure image for two large-exposure-ratio images from an emerging high dynamic range (HDR) video capturing device. The framework is set up by fusing conventional and deep learning methods. Experimental results indicate that the deep learning method can be used to improve the quality of interpolated image via the conventional method significantly. The conventional method can be adopted to increase the convergence speed of the deep learning method and to reduce the number of samples which is required by the deep learning method. They compensate each other.