A Novel Deep CNN-based Tone Mapping Operator and Wide Dynamic Range Imaging Applications

Lin, Mengchen

A Novel Deep CNN-based Tone Mapping Operator and Wide Dynamic Range Imaging Applications

dc.contributor.advisor	Jacobson, Michael John
dc.contributor.advisor	Yadid-Pecht, Orly
dc.contributor.author	Lin, Mengchen
dc.contributor.committeemember	Dimitrov, Vassil S.
dc.contributor.committeemember	Safavi-Naini, Reihaneh S.
dc.date	2021-02
dc.date.accessioned	2020-12-22T21:25:55Z
dc.date.available	2020-12-22T21:25:55Z
dc.date.issued	2020-12
dc.description.abstract	This thesis addresses three Wide Dynamic Range (WDR) imaging and application problems. First, we present a novel tone mapping solution using a reformulated Laplacian pyramid and deep learning. The reformulated Laplacian pyramid constantly decomposes a WDR image into two frequency bands where the low-frequency band is global feature-oriented, and the high-frequency band is local feature-oriented. The reformulation preserves the local features in their original resolution and condenses the global features into a low-resolution image. The generated frequency bands are reconstructed and fine-tuned to output the final tone mapped image that can reveal more detail and contrast. Then we discuss the ability of the model to recover and enhance the lost detail of a degraded. We adopt the same theory to build a deep single image enhancer. Our model outperforms the state-of-the-art methods. Next, we propose a WDR dataset for face detection, named WDR FACE, to facilitate and support future face detection research in the WDR field. This dataset contains a total of 398 16-bit megapixel grayscale WDR images collected from 29 subjects and eight selected WDR scenes. The dynamic range of 90\% images surpasses 60,000:1. We also provide the preliminary experimental results of face detection with 25 different tone mapping operators and five different face detectors on this WDR dataset. We provide preliminary results of face detection experiments as a reference for researchers using this data set in the future. Finally, we applied WDR imaging techniques to Magnetic Resonance (MR) imaging to provide better visualization of the images to physicians. We carried out a feasibility study using domain expert observation to show that it is possible to assist physicians' diagnosis of knee lesion injuries via MR image contrast enhancement using different tone mapping algorithms.	en_US
dc.identifier.citation	Lin, M. (2020). A novel deep CNN-based tone mapping operator and wide dynamic range imaging applications (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.	en_US
dc.identifier.doi	http://dx.doi.org/10.11575/PRISM/38493
dc.identifier.uri	http://hdl.handle.net/1880/112899
dc.language.iso	eng	en_US
dc.publisher.faculty	Science	en_US
dc.publisher.institution	University of Calgary	en
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.	en_US
dc.subject	Deep Learning	en_US
dc.subject	Tong Mapping	en_US
dc.subject	Wide Dynamic Range Imaging	en_US
dc.subject	High Dynamic Range Imaging	en_US
dc.subject	Single Image Contrast Enhancement	en_US
dc.subject	MRI	en_US
dc.subject	Database	en_US
dc.subject	Image Processing	en_US
dc.subject.classification	Computer Science	en_US
dc.title	A Novel Deep CNN-based Tone Mapping Operator and Wide Dynamic Range Imaging Applications	en_US
dc.type	master thesis	en_US
thesis.degree.discipline	Computer Science	en_US
thesis.degree.grantor	University of Calgary	en_US
thesis.degree.name	Master of Science (MSc)	en_US
ucalgary.item.requestcopy	true	en_US