Fully Convolutional Networks Based Reflection Separation for Light Field Images

• Main Authors: Ruei-Yu Chang, 張瑞宇
• Other Authors: Chih-Wei Tang
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/k6m8qh

碩士 === 國立中央大學 === 通訊工程學系 === 107 === Existing reflection separation schemes designed for multi-view images cannot be applied to light filed images due to the dense light fields with narrow baselines. In order to improve accuracy of the reconstructed background (i.e., the transmitted layer), most light field data based reflection separation schemes estimate a disparity map before reflection separation. Different from previous work, this thesis uses the existing EPINET based on disparity estimation of light field image without reflection, and separates the mixed light field image of weak reflection. At the training stage, the network takes multi-view images stacks along principle directions of light field data as inputs, and significant convolution features of the background layer are learned in an end-to-end manner. Then the FCN learns to predict pixel-wise gray-scale values of the background layer of the central view. Our experimental results show that the background layer can be reconstructed effectively by using EPINET and the mixed light field image dataset proposed in this thesis.