Abstract
Digital cameras with a single sensor use a color filter array (CFA) that captures only one color component in each pixel. Therefore, noise and artifacts will be generated when reconstructing the color image, which reduces the resolution of the image. In this paper, we proposed an image demosaicing method based on generative adversarial network (GAN) to obtain high-quality color images. The proposed network does not need any initial interpolation process in the data preparation phase, which can greatly reduce the computational complexity. The generator of the GAN is designed using the U-net to directly generate the demosaicing images. The dense residual network is used for the discriminator to improve the discriminant ability of the network. We compared the proposed method with several interpolation-based algorithms and the DnCNN. Results from the comparative experiments proved that the proposed method can more effectively eliminate the image artifacts and can better recover the color image.
Highlights
Images are widely used in people’s daily life
Numerical experiments showed that the proposed algorithm can effectively reduce the artifacts at the edges and produce near-real reconstructed images, which can be the basis for subsequent image processing, such as image recognition and image transmission. e proposed method can produce better recovered color images; the learning-based strategy is relatively time-consuming in the data training phase. erefore, how to improve the efficiency of the network training is an important aspect to further enhance the performance of the learning-based technology
We proposed an image demosaicing method based on generative adversarial network (GAN). e generator is designed by using the improved U-net architecture to directly generate the demosaicing images
Summary
Images are widely used in people’s daily life. Compared to analog images, digital images are more superior in their higher resolution and easier storage, and they are more suitable for computer processing. As only one color component is captured for each pixel in the CFA, without image demosaicing, the CFA image can only reflect the general outline of the scenery instead of the complete color information, which affects subsequent image processing [8]. (1) We proposed a CFA image demosaicing method based on GAN (2) We carefully designed each part for the GAN model (3) We introduced long jump connections for the improved U-net [20] model to design the generator (4) We used the dense residual network, which includes dense residual blocks with long jump links and dense connections for the discriminator (5) We combined the adversarial loss, the feature loss, and the pixel loss together to further strengthen the network performance. E results prove that the proposed method can more effectively remove artifacts and recover the full-color image, especially for some high-frequency areas such as edges and angles We show the performance of our method using some comparative experiments. e results prove that the proposed method can more effectively remove artifacts and recover the full-color image, especially for some high-frequency areas such as edges and angles
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