Noise Modelling for Smartphone Cameras

Simple item page
dc.contributor.advisorBrown, Michael S.
dc.contributor.authorAbdelhamed, Abdelrahman Kamel Siddek
dc.date.accessioned2021-03-08T17:18:59Z
dc.date.available2021-03-08T17:18:59Z
dc.date.copyright2020-11
dc.date.issued2021-03-08
dc.date.updated2021-03-08T17:18:59Z
dc.degree.disciplineComputer Science
dc.degree.levelDoctoral
dc.degree.namePhD - Doctor of Philosophy
dc.description.abstractIn our everyday life we capture photographs. We create these images by measuring the amount of light, radiated from scenes in our physical world, on camera sensors embedded in our smartphones. Image noise is variation in the measurement of intensities or colours in digital images and it has the undesirable effect of obscuring information in images. Image noise is produced from two main sources: (1) the unavoidable, random nature of light and (2) the imaging sensor and associated circuitry. Unlike professional cameras, smartphone cameras have much smaller imaging sensors which makes them more susceptible to higher and more complex noise. To model, and ultimately remove, image noise, many mathematical models have been proposed. These models either represent synthetic noise or rely on assumptions that makes them unable to model real noise distributions observed from empirical data. One major reason for that is the lack of sufficient real noisy image datasets with ground truth images that can enable the study of real camera noise. The purpose of this dissertation is to provide a study on image noise modelling based on data-driven approaches specific to smartphone cameras. To this end, we first propose a systematic method for estimating ground truth noise-free images from noisy images captured by smartphone cameras. Using the proposed method, we collect a large-scale dataset, termed the Smartphone Image Denoising Dataset (SIDD), of high-quality images that can be used for noise modelling. Next, we utilize the SIDD dataset to devise a generative noise model, termed Noise Flow, that can be used to synthesise realistic noisy images to be utilized in many computer vision tasks. We also use our datatset to provide a benchmark for image denoising algorithms on real noisy images. As part of this benchmarking effort, we have developed an online image denoising challenge with the necessary software tools to facilitate the evaluation of image denoising methods applied to realistic noisy images. We believe the work in this dissertation helps to advance the state of the art in image noise modelling and image denoising.
dc.identifier.urihttp://hdl.handle.net/10315/38163
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectComputer science
dc.subject.keywordsnoise modelling
dc.subject.keywordssmartphone cameras
dc.subject.keywordsimage noise
dc.subject.keywordsimage denoising
dc.subject.keywordsground truth images
dc.subject.keywordsnoise estimation
dc.subject.keywordsnoise dataset
dc.subject.keywordsdenoising dataset
dc.subject.keywordsSIDD
dc.subject.keywordssmartphone image denoising dataset
dc.subject.keywordsnoise flow
dc.subject.keywordsnormalizing flows
dc.subject.keywordsnoise model
dc.subject.keywordsimage processing
dc.subject.keywordsdenoising benchmark
dc.subject.keywordsNTIRE challenge
dc.subject.keywordscomputational imaging
dc.subject.keywordscomputer vision
dc.titleNoise Modelling for Smartphone Cameras
dc.typeElectronic Thesis or Dissertation

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Abdelhamed_Abdelrahman_KS_2020_PhD.pdf
Size:
23.69 MB
Format:
Adobe Portable Document Format
Description:
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 and Engineering

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