Dual Disparity-Based Novel View Reconstruction for Light Field Images Using Discrete Cosine Transform Filter

Abstract

Compared to conventional photography which allows capturing spatial intensity only, light field imaging can capture angular and spatial information by collecting information from all directions. This massive information could be used in many applications such as depth estimation and post-capture refocusing. In addition, the emergence of consumer light field cameras has resulted in the widespread use of light field imaging. However, its limited resolution constitutes a major drawback to the use of enormous capabilities provided. In this article, we tried to alleviate the effect of this drawback by using a machine learning algorithm. Our proposed network is built upon existing reconstruction techniques that divide the process into disparity estimation and final image reconstruction. This is achieved by using two consecutive neural networks while the whole network is trained simultaneously. We propose to use a predefined convolutional network at the first stage to decrease preprocessing time, and in addition, we use dual disparity vectors to alleviate the interpolation error whereas warping input images. Our system was trained to reconstruct multi-angular resolution images fast and accurately using real light field images for training. Experimental results demonstrate that the proposed system can reconstruct high-quality images faster than the state-of-the-art techniques.