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

• URL: http://arxiv.org/abs/2206.01813v1
• Date: Fri, 3 Jun 2022 20:43:17 GMT
• Title: Learning sRGB-to-Raw-RGB De-rendering with Content-Aware Metadata
• Authors: Seonghyeon Nam, Abhijith Punnappurath, Marcus A. Brubaker, Michael S. Brown
• Abstract summary: We show 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.
• Score: 46.28281823015191
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• BSRAW: Improving Blind RAW Image Super-Resolution [63.408484584265985]
  We tackle blind image super-resolution in the RAW domain. We design a realistic degradation pipeline tailored specifically for training models with raw sensor data. Our BSRAW models trained with our pipeline can upscale real-scene RAW images and improve their quality.
  arXiv  Detail & Related papers  (2023-12-24T14:17:28Z)
• Image Demoireing in RAW and sRGB Domains [18.921026683632146]
  We develop Skip-Connection-based Demoireing Module (SCDM) with Gated Feedback Module (GFM) and Frequency Selection Module (FSM) We design a RGB Guided ISP (RGISP) to learn a device-dependent ISP, assisting the process of color recovery. Our RRID outperforms state-of-the-art approaches, in terms of the performance in moire pattern removal and color cast correction by 0.62dB in PSNR and 0.003 in SSIM.
  arXiv  Detail & Related papers  (2023-12-14T16:00:28Z)
• Self-Supervised Reversed Image Signal Processing via Reference-Guided Dynamic Parameter Selection [1.1602089225841632]
  We propose a self-supervised reversed ISP method that does not require metadata and paired images. The proposed method converts a RGB image into a RAW-like image taken in the same environment with the same sensor as a reference RAW image. We show that the proposed method is able to learn various reversed ISPs with comparable accuracy to other state-of-the-art supervised methods.
  arXiv  Detail & Related papers  (2023-03-24T11:12:05Z)
• Raw Image Reconstruction with Learned Compact Metadata [61.62454853089346]
  We propose a novel framework to learn a compact representation in the latent space serving as the metadata in an end-to-end manner. We show how the proposed raw image compression scheme can adaptively allocate more bits to image regions that are important from a global perspective.
  arXiv  Detail & Related papers  (2023-02-25T05:29:45Z)
• Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report [109.2135194765743]
  This paper introduces the AIM 2022 Challenge on Reversed Image Signal Processing and RAW Reconstruction. We aim to recover raw sensor images from the corresponding RGBs without metadata and, by doing this, "reverse" the ISP transformation.
  arXiv  Detail & Related papers  (2022-10-20T10:43:53Z)
• Learning RAW-to-sRGB Mappings with Inaccurately Aligned Supervision [76.41657124981549]
  This paper presents a joint learning model for image alignment and RAW-to-sRGB mapping. Experiments show that our method performs favorably against state-of-the-arts on ZRR and SR-RAW datasets.
  arXiv  Detail & Related papers  (2021-08-18T12:41:36Z)
• Semantic-embedded Unsupervised Spectral Reconstruction from Single RGB Images in the Wild [48.44194221801609]
  We propose a new lightweight and end-to-end learning-based framework to tackle this challenge. We progressively spread the differences between input RGB images and re-projected RGB images from recovered HS images via effective camera spectral response function estimation. Our method significantly outperforms state-of-the-art unsupervised methods and even exceeds the latest supervised method under some settings.
  arXiv  Detail & Related papers  (2021-08-15T05:19:44Z)
• Invertible Image Signal Processing [42.109752151834456]
  Invertible Image Signal Processing (InvISP) pipeline enables rendering visually appealing sRGB images. We can reconstruct realistic RAW data instead of synthesizing RAW data from sRGB images without any memory overhead.
  arXiv  Detail & Related papers  (2021-03-28T06:30:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.