Modeling sRGB Camera Noise with Normalizing Flows

Simple item page
dc.contributor.advisorBrubaker, Marcus
dc.contributor.authorKousha, Shayan
dc.date.accessioned2022-08-08T15:52:40Z
dc.date.available2022-08-08T15:52:40Z
dc.date.copyright2022-04-14
dc.date.issued2022-08-08
dc.date.updated2022-08-08T15:52:40Z
dc.degree.disciplineComputer Science
dc.degree.levelMaster's
dc.degree.nameMSc - Master of Science
dc.description.abstractNoise modeling and reduction are fundamental tasks in low-level computer vision. They are particularly important for smartphone cameras relying on small sensors that exhibit visually noticeable noise. There has recently been renewed interest in using data-driven approaches to improve camera noise models via neural networks. These data-driven approaches target noise present in the raw-sensor image before it has been processed by the camera's image signal processor (ISP). Modeling noise in the RAW-rgb domain is useful for improving and testing the in-camera denoising algorithm; however, there are situations where the camera's ISP does not apply denoising or additional denoising is desired when the RAW-rgb domain image is no longer available. In such cases, the sensor noise propagates through the ISP to the final rendered image encoded in standard RGB (sRGB). The nonlinear steps on the ISP culminate in a significantly more complex noise distribution in the sRGB domain and existing raw-domain noise models are unable to capture the sRGB noise distribution. We propose a new sRGB-domain noise model based on normalizing flows that is capable of learning the complex noise distribution found in sRGB images under various ISO levels. Our normalizing flows-based approach outperforms other models by a large margin in noise modeling and synthesis tasks. We also show that image denoisers trained on noisy images synthesized with our noise model outperforms those trained with noise from baseline models.
dc.identifier.urihttp://hdl.handle.net/10315/39628
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectArtificial intelligence
dc.subject.keywordsComputer science
dc.subject.keywordsComputer vision
dc.subject.keywordsMachine learning
dc.subject.keywordsDeep learning
dc.subject.keywordsNormalizing flows
dc.subject.keywordsNoise modeling
dc.subject.keywordsCamera noise modeling
dc.subject.keywordssRGB noise modeling
dc.subject.keywordssRGB
dc.subject.keywordsNoise flow
dc.titleModeling sRGB Camera Noise with Normalizing Flows
dc.typeElectronic Thesis or Dissertation

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Kousha_Shayan_2022_Masters.pdf
Size:
2.84 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 2 of 2
No Thumbnail Available
Name:
license.txt
Size:
1.87 KB
Format:
Plain Text
Description:
Download
No Thumbnail Available
Name:
YorkU_ETDlicense.txt
Size:
3.39 KB
Format:
Plain Text
Description:
Download

Collections

Computer Science

All items in the YorkSpace institutional repository are protected by copyright, with all rights reserved except where explicitly noted.