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

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