Learning to Zoom Inside Camera Imaging Pipeline

Abstract

Existing single image super-resolution methods are either designed for synthetic data, or for real data but in the RGB-to-RGB or the RAW-to-RGB domain. This paper proposes to zoom an image from RAW to RAW inside the camera imaging pipeline. The RAW-to-RAW domain closes the gap between the ideal and the real degradation models. It also excludes the image signal processing pipeline, which refocuses the model learning onto the super-resolution. To these ends, we design a method that receives a low-resolution RAW as the input and estimates the desired higher-resolution RAW jointly with the degradation model. In our method, two convolutional neural networks are learned to constrain the high-resolution image and the degradation model in lower-dimensional subspaces. This subspace constraint converts the ill-posed SISR problem to a well-posed one. To demonstrate the superiority of the proposed method and the RAW-to-RAW domain, we conduct evaluations on the RealSR and the SR-RAW datasets. The results show that our method performs superiorly over the state-of-the-arts both qualitatively and quantitatively, and it also generalizes well and enables zero-shot transfer across different sensors.