Invertible Image Signal Processing
- URL: http://arxiv.org/abs/2103.15061v1
- Date: Sun, 28 Mar 2021 06:30:15 GMT
- Title: Invertible Image Signal Processing
- Authors: Yazhou Xing, Zian Qian, Qifeng Chen
- Abstract summary: 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.
- Score: 42.109752151834456
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Unprocessed RAW data is a highly valuable image format for image editing and
computer vision. However, since the file size of RAW data is huge, most users
can only get access to processed and compressed sRGB images. To bridge this
gap, we design an Invertible Image Signal Processing (InvISP) pipeline, which
not only enables rendering visually appealing sRGB images but also allows
recovering nearly perfect RAW data. Due to our framework's inherent
reversibility, we can reconstruct realistic RAW data instead of synthesizing
RAW data from sRGB images without any memory overhead. We also integrate a
differentiable JPEG compression simulator that empowers our framework to
reconstruct RAW data from JPEG images. Extensive quantitative and qualitative
experiments on two DSLR demonstrate that our method obtains much higher quality
in both rendered sRGB images and reconstructed RAW data than alternative
methods.
Related papers
- Raw Instinct: Trust Your Classifiers and Skip the Conversion [12.323236593352698]
We show that a sufficiently advanced classifier can yield equivalent results on RAW input compared to RGB.
We furthermore show that the total computation time from RAW image data to classification results for RAW images can be up to 8.46 times faster than RGB.
arXiv Detail & Related papers (2024-03-21T14:45:41Z) - 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) - 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) - Efficient Visual Computing with Camera RAW Snapshots [41.9863557302409]
Conventional cameras capture image irradiance on a sensor and convert it to RGB images using an image signal processor (ISP)
One can argue that since RAW images contain all the captured information, the conversion of RAW to RGB using an ISP is not necessary for visual computing.
We propose a novel $rho$-Vision framework to perform high-level semantic understanding and low-level compression using RAW images.
arXiv Detail & Related papers (2022-12-15T12:54:21Z) - 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 sRGB-to-Raw-RGB De-rendering with Content-Aware Metadata [46.28281823015191]
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.
arXiv Detail & Related papers (2022-06-03T20:43:17Z) - Towards Low Light Enhancement with RAW Images [101.35754364753409]
We make the first benchmark effort to elaborate on the superiority of using RAW images in the low light enhancement.
We develop a new evaluation framework, Factorized Enhancement Model (FEM), which decomposes the properties of RAW images into measurable factors.
A RAW-guiding Exposure Enhancement Network (REENet) is developed, which makes trade-offs between the advantages and inaccessibility of RAW images in real applications.
arXiv Detail & Related papers (2021-12-28T07:27:51Z)
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.