Image demosaicing - one of the most important early stages in digital camera
pipelines - addressed the problem of reconstructing a full-resolution image
from so-called color-filter-arrays. Despite tremendous progress made in the
pase decade, a fundamental issue that remains to be addressed is how to assure
the visual quality of reconstructed images especially in the presence of noise
corruption. Inspired by recent advances in generative adversarial networks
(GAN), we present a novel deep learning approach toward joint demosaicing and
denoising (JDD) with perceptual optimization in order to ensure the visual
quality of reconstructed images. The key contributions of this work include: 1)
we have developed a GAN-based approach toward image demosacing in which a
discriminator network with both perceptual and adversarial loss functions are
used for quality assurance; 2) we propose to optimize the perceptual quality of
reconstructed images by the proposed GAN in an end-to-end manner. Such
end-to-end optimization of GAN is particularly effective for jointly exploiting
the gain brought by each modular component (e.g., residue learning in the
generative network and perceptual loss in the discriminator network). Our
extensive experimental results have shown convincingly improved performance
over existing state-of-the-art methods in terms of both subjective and
objective quality metrics with a comparable computational cost.

该文提出了一种基于生成对抗网络的联合去马赛克和去噪方法，通过感知优化保证恢复图像的视觉质量，其结果在客观和主观质量指标上表现出优于现有先进技术的结果，且有可比性的计算成本。