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Relevant bibliographies by topics / Low-light images

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Dissertations / Theses

Academic literature on the topic 'Low-light images'

Author: Grafiati

Published: 6 September 2023

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Dissertations / Theses on the topic "Low-light images"

1

McKoen, K. M. H. H. "Digital restoration of low light level video images." Thesis, Imperial College London, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343720.

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2

Sankaran, Sharlini. "The influence of ambient light on the detectability of low-contrast lesions in simulated ultrasound images." Ohio : Ohio University, 1999. http://www.ohiolink.edu/etd/view.cgi?ohiou1175627273.

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3

Авраменко, Віктор Васильович, Виктор Васильевич Авраменко, Viktor Vasylovych Avramenko, and К. Salnik. "Recognition of fragments of standard images at low light level and the presence of additive impulsive noise." Thesis, Sumy State University, 2017. http://essuir.sumdu.edu.ua/handle/123456789/55739.

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On the basis of integral disproportion function of the first-order the algorithm recognizing fragments of standards is created. It works in low light image that is analyzed and the presence of additive impulse noise. This algorithm permits to fin an appropriate pixel in one of several standards for each pixel of the image.

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4

Landin, Roman. "Object Detection with Deep Convolutional Neural Networks in Images with Various Lighting Conditions and Limited Resolution." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-300055.

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Computer vision is a key component of any autonomous system. Real world computer vision applications rely on a proper and accurate detection and classification of objects. A detection algorithm that doesn’t guarantee reasonable detection accuracy is not applicable in real time scenarios where safety is the main objective. Factors that impact detection accuracy are illumination conditions and image resolution. Both contribute to degradation of objects and lead to low classifications and detection accuracy. Recent development of Convolutional Neural Networks (CNNs) based algorithms offers possib

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5

Vorhies, John T. "Low-complexity Algorithms for Light Field Image Processing." University of Akron / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=akron1590771210097321.

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6

Miller, Sarah Victoria. "Mulit-Resolution Aitchison Geometry Image Denoising for Low-Light Photography." University of Dayton / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1596444315236623.

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7

Zhao, Ping. "Low-Complexity Deep Learning-Based Light Field Image Quality Assessment." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/25977.

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Light field image quality assessment (LF-IQA) has attracted increasing research interests due to the fast-growing demands for immersive media experience. The majority of existing LF-IQA metrics, however, heavily rely on high-complexity statistics-based feature extraction for the quality assessment task, which will be hardly sustainable in real-time applications or power-constrained consumer electronic devices in future real-life applications. In this research, a low-complexity Deep learning-based Light Field Image Quality Evaluator (DeLFIQE) is proposed to automatically and efficiently extra

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8

Anzagira, Leo. "Imaging performance in advanced small pixel and low light image sensors." Thesis, Dartmouth College, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10144602.

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<p> Even though image sensor performance has improved tremendously over the years, there are two key areas where sensor performance leaves room for improvement. Firstly, small pixel performance is limited by low full well, low dynamic range and high crosstalk, which greatly impact the sensor performance. Also, low light color image sensors, which use color filter arrays, have low sensitivity due to the selective light rejection by the color filters. The quanta image sensor (QIS) concept was proposed to mitigate the full well and dynamic range issues in small pixel image sensors. In this concep

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9

Hurle, Bernard Alfred. "The charge coupled device as a low light detector in beam foil spectroscopy." Thesis, University of Kent, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.332296.

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10

Raventos, Joaquin. "New Test Set for Video Quality Benchmarking." Digital Commons @ East Tennessee State University, 2011. https://dc.etsu.edu/etd/1226.

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A new test set design and benchmarking approach (US Patent pending) allows a "standard observer" to assess the end-to-end image quality characteristics of video imaging systems operating in day time or low-light conditions. It uses randomized targets based on extensive application of Photometry, Geometrical Optics, and Digital Media. The benchmarking takes into account the target’s contrast sensitivity, its color characteristics, and several aspects of human vision such as visual acuity and dynamic response. The standard observer is part of the "extended video imaging system" (EVIS). The new t

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