Improving quality of scanned visual content using convolutional neural networks

master's thesis

Jovan Toroman (Author), Luka Čehovin (Mentor)

Abstract

In this work we approach the problem of image quality improvement for images of documents captured using smartphones. Our goal is to make content captured this way as similar as possible to the original groundtruth images. We make a twofold contribution, (1) we propose an innovative method for improving quality of documents using convolutional neural networks and (2) we create a training dataset containing images captured using smartphones under dierent external conditions (lighting, viewing angle). This dataset is captured under controlled external conditions using an acquisition setup developed for this purpose. In our work we use six dierent smartphones and one hundred groundtruth images. We build our work on two different existing convolutional neural network architectures, UNet and DPED network, using them as our starting point. Both models are adapted to our domain. We experiment with dierent hyperparameters for both networks, as well as with dierent forms of training. We evaluate results of the two variants of our method against each other and also against other baseline approaches (simple contrast enhancer and a function from Adobe Photoshop Express). We use standard image quality comparison metrics to objectively compare performance. From the results we can see that our work outperforms baseline approaches, especially in more dicult scenarios of uneven illumination. Finally, we discuss the results of our method and possible improvements.

Keywords

captured document quality enhancement;smartphone scanner;convolutional neural networks;computer science;computer and information science;master's degree;

Data

Language:	English
Year of publishing:	2020
Typology:	2.09 - Master's Thesis
Organization:	UL FRI - Faculty of Computer and Information Science
Publisher:	[J. Toroman]
UDC:	004.8(043.2)
COBISS:	42265859
Views:	436
Downloads:	128
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary title:	Izboljšava kvalitete zajetega slikovnega gradiva s konvolucijskimi nevronskimi mrežami
Secondary abstract:	V tem delu se lotevamo problema izboljšanja kakovosti slik slikovnih dokumentov, posnetih s pametnimi telefoni. Naš cilj je dobiti slike čim bolj podobne prvotnim referenčnim slikam. Predlagamo dva prispevka prispevka, (1) predlagamo inovativno metodo za izboljšanje kakovosti slik s konvolucijskimi nevronskimi mrežami, in (2) ustvarimo podatkovno zbirko, ki vsebuje slike, posnete s pametnimi telefoni v različnih zunanjih pogojih (osvetlitev, kot gledanja). Slike so zajete v nadzorovanih zunanjih pogojih z uporabo naprave, razvite za namen tega dela. Za zajem smo uporabili šest različnih pametnih telefonov, zajeli smo sto različnih izvornih slik. Naša delo temelji na dveh konvolucijskih arhitekturah nevronskih mrež, UNet in DPED. Oba modela sta prilagojena za uporabo v naši problemski domeni, preizkusimo več kombinacij hiperparametrov ter načinov učenja. Rezultate obeh različic naše metode primerjamo med seboj in tudi glede na dva referenčna pristopa (preprost ojačevalec kontrasta in rešitev, dostopna v programu Adobe Photoshop Express). Za primerjavo uporabljamo standarne mere za objektivno ocenjevanje podobnosti slik. Iz rezultatov vidimo, da naša metoda deluje bolje kot referenčne metode, še posebej v težjih pogojih z neenakomerno osvetlitvijo. V zadnjem delu rezultate tudi komentiramo in izpostavimo možnosti za nadaljnje delo.
Secondary keywords:	izboljšava kakovosti slik;optični čitalnik za pametne telefone;konvolucijske nevronske mreže;računalništvo;računalništvo in informatika;magisteriji;
Type (COBISS):	Master's thesis/paper
Study programme:	1000471
Embargo end date (OpenAIRE):	1970-01-01
Thesis comment:	Univ. v Ljubljani, Fak. za računalništvo in informatiko
Pages:	XII, 74 str.
ID:	12241514