Learning sRGB-to-Raw-RGB De-rendering with Content-Aware Metadata

06/03/2022
by   Seonghyeon Nam, et al.
0

Most camera images are rendered and saved in the standard RGB (sRGB) format by the camera's hardware. Due to the in-camera photo-finishing routines, nonlinear sRGB images are undesirable for computer vision tasks that assume a direct relationship between pixel values and scene radiance. For such applications, linear raw-RGB sensor images are preferred. Saving images in their raw-RGB format is still uncommon due to the large storage requirement and lack of support by many imaging applications. Several "raw reconstruction" methods have been proposed that utilize specialized metadata sampled from the raw-RGB image at capture time and embedded in the sRGB image. This metadata is used to parameterize a mapping function to de-render the sRGB image back to its original raw-RGB format when needed. Existing raw reconstruction methods rely on simple sampling strategies and global mapping to perform the de-rendering. This paper shows how to improve the de-rendering results by jointly learning sampling and reconstruction. Our experiments show that our learned sampling can adapt to the image content to produce better raw reconstructions than existing methods. We also describe an online fine-tuning strategy for the reconstruction network to improve results further.

READ FULL TEXT

page 6

page 12

page 13

page 14

page 15

page 16

page 17

page 18

06/23/2020

CIE XYZ Net: Unprocessing Images for Low-Level Computer Vision Tasks

Cameras currently allow access to two image states: (i) a minimally proc...
04/13/2021

Learning to Jointly Deblur, Demosaick and Denoise Raw Images

We address the problem of non-blind deblurring and demosaicking of noisy...
07/26/2017

Modelling the Scene Dependent Imaging in Cameras with a Deep Neural Network

We present a novel deep learning framework that models the scene depende...
09/08/2021

Matching in the Dark: A Dataset for Matching Image Pairs of Low-light Scenes

This paper considers matching images of low-light scenes, aiming to wide...
12/16/2021

All You Need is RAW: Defending Against Adversarial Attacks with Camera Image Pipelines

Existing neural networks for computer vision tasks are vulnerable to adv...
01/25/2021

ISP Distillation

Nowadays, many of the images captured are "observed" by machines only an...
01/10/2022

Model-Based Image Signal Processors via Learnable Dictionaries

Digital cameras transform sensor RAW readings into RGB images by means o...