Learning a Deep Convolutional Network for Demosaicking
Date Issued
2016
Date
2016
Author(s)
Syu, Nai-Sheng
Abstract
This thesis presents a demosaicking method based on the convolutional neural network (CNN). Our method learns an end-to-end mapping between the mosaic samples and the original image with full information. The comprehensive evaluation with 10 competitive methods on the popular benchmark confirms that the data-driven, automatically learned features from CNN can be more effective and the proposed method outperforms the current state-ofthe-art algorithms. The proposed framework has also been proved effective for a more general but challenging task, spatially varying exposure and color (SVEC) demosaicking, for reconstructing an HDR image from a single shot. In addition, based on the observation that previous literatures usually develop their algorithms on pattern design or demosaicking separately. In this thesis we combine these two problems and form an optimization problem with CNN by introducing a new layer, pattern layer. Experiments show the better performances than Bayer-CFA-based demosaicking CNN on both PSNR value and visual quality.
Subjects
convolutional neural network
demosaicking
Type
thesis