  We present a novel deep learning framework that models the scene dependent
image processing inside cameras. Often called as the radiometric calibration,
the process of recovering RAW images from processed images (JPEG format in the
sRGB color space) is essential for many computer vision tasks that rely on
physically accurate radiance values. All previous works rely on the
deterministic imaging model where the color transformation stays the same
regardless of the scene and thus they can only be applied for images taken
under the manual mode. In this paper, we propose a data-driven approach to
learn the scene dependent and locally varying image processing inside cameras
under the automode. Our method incorporates both the global and the local scene
context into pixel-wise features via multi-scale pyramid of learnable histogram
layers. The results show that we can model the imaging pipeline of different
cameras that operate under the automode accurately in both directions (from RAW
to sRGB, from sRGB to RAW) and we show how we can apply our method to improve
the performance of image deblurring.
