Abstract
Demosaicing, denoising and super-resolution (SR) are of practical importance in digital image processing and have been studied independently in the passed decades. Despite the recent improvement of learning-based image processing methods in image quality, there lacks enough analysis into their interactions and characteristics under a realistic setting of the mixture problem of demosaicing, denoising and SR. In existing solutions, these tasks are simply combined to obtain a high-resolution image from a low-resolution raw mosaic image, resulting in a performance drop of the final image quality. In this paper, we first rethink the mixture problem from a holistic perspective and then propose the Trinity Enhancement Network (TENet), a specially designed learning-based method for the mixture problem, which adopts a novel image processing pipeline order and a joint learning strategy. In order to obtain the correct color sampling for training, we also contribute a new dataset namely PixelShift200, which consists of high-quality full color sampled real-world images using the advanced pixel shift technique. Experiments demonstrate that our TENet is superior to existing solutions in both quantitative and qualitative perspective. Our experiments also show the necessity of the proposed PixelShift200 dataset.
Abstract (translated)
去噪、去噪和超分辨率（SR）在数字图像处理中具有重要的实际意义，近几十年来一直在独立研究。尽管近年来基于学习的图像处理方法在图像质量方面有所改进，但在去除、去噪和SR混合问题的现实背景下，对它们的相互作用和特性分析还不够，在现有的解决方案中，这些任务只是简单地结合在一起，从低分辨率图像中获得高分辨率图像。解决原始马赛克图像，导致最终图像质量性能下降。本文首先从整体的角度对混合问题进行了重新思考，然后提出了一种针对混合问题专门设计的基于学习的三位一体增强网络（TENET），它采用了一种新颖的图像处理流水线顺序和联合学习策略。为了获得训练所需的正确颜色采样，我们还提供了一个新的数据集，即Pixelshift200，它由使用高级像素移位技术的高质量全彩色采样现实世界图像组成。实验表明，无论从定量还是定性的角度，我们的原则都优于现有的解决方案。我们的实验也证明了Pixelshift200数据集的必要性。
URL
https://arxiv.org/abs/1905.02538
