Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/152819
Full metadata record
DC FieldValueLanguage
dc.contributor.authorZheng, Bowenen_US
dc.date.accessioned2021-10-05T04:22:45Z-
dc.date.available2021-10-05T04:22:45Z-
dc.date.issued2021-
dc.identifier.citationZheng, B. (2021). Learning point and contextual processing networks for low-light image enhancement. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/152819en_US
dc.identifier.urihttps://hdl.handle.net/10356/152819-
dc.description.abstractPoint processing techniques, such as the gamma correction, are classical methods for low-light image enhancement. These methods are efficient, explicit, and interpretable. However, to tackle images of various light conditions, these methods need to be set meticulously and accordingly. To tackle this issue, I propose to combine these traditional methods with the powerful learning ability of convolutional neural networks (CNNs). Specifically, I propose a point and contextual processing network (PCPNet) consisting of two parallel branches: In the point processing branch, given an input image, a set of hidden intensities images (HIIs) are predicted, as well as a set of parameters governing the gamma corrections performing on the HIIs. Due to the nonlinearity of the gamma corrections, we can obtain diverse and complex enhancement effects merely with a shallow network (of 3 layers). This further guarantees the efficiency of the point processing branch, and allows the HIIs to be generated with the full resolution and preserve the details. While in the contextual processing branch, an encoder-decoder structure is adopted to explore contextual information, which helps to alleviate the effects of noise. The outputs of these two branches are combined as the final enhancement result. Extensive experiments on multiple datasets, including LOL, MIT, and ExDark, validate the effectiveness of the proposed method.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).en_US
dc.subjectEngineering::Computer science and engineering::Computing methodologies::Image processing and computer visionen_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleLearning point and contextual processing networks for low-light image enhancementen_US
dc.typeThesis-Master by Researchen_US
dc.contributor.supervisorJiang Xudongen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Engineeringen_US
dc.identifier.doi10.32657/10356/152819-
dc.contributor.supervisoremailEXDJiang@ntu.edu.sgen_US
item.fulltextWith Fulltext-
item.grantfulltextopen-
Appears in Collections:EEE Theses
Files in This Item:
File Description SizeFormat 
Amended_Thesis_ZHENGBOWEN_G1900930F .pdf1.82 MBAdobe PDFView/Open

Page view(s)

121
Updated on May 20, 2022

Download(s) 50

44
Updated on May 20, 2022

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.