Learning Deep Convolutional Networks for Demosaicing

Understand

This paper presents a comprehensive study of applying the convolutional neural network (CNN) to solving the demosaicing problem.

• The paper presents two CNN models that learn end-to-end mappings between the mosaic samples and the original image patches with full information.
• In the case the Bayer color filter array (CFA) is used, an evaluation with ten competitive methods on popular benchmarks confirms that the data-driven, automatically learned features by the CNN models are very effective.
• Experiments show that the proposed CNN models can perform equally well in both the sRGB space and the linear space.