CNN for license plate motion deblurring

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F16%3APU122835" target="_blank" >RIV/00216305:26230/16:PU122835 - isvavai.cz</a>

Result on the web

<a href="http://ieeexplore.ieee.org/document/7533077/" target="_blank" >http://ieeexplore.ieee.org/document/7533077/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICIP.2016.7533077" target="_blank" >10.1109/ICIP.2016.7533077</a>

Result language

angličtina

Original language name

CNN for license plate motion deblurring

Original language description

In this work we explore the previously proposed approach of direct blind deconvolution and denoising with convolutional neural networks (CNN) in a situation where the blur kernels are partially constrained. We focus on blurred images from a real-life traffic surveillance system, on which we, for the first time, demonstrate that neural networks trained on artificial data provide superior reconstruction quality on real images compared to traditional blind deconvolution methods. The training data is easy to obtain by blurring sharp photos from a target system with a very rough approximation of the expected blur kernels, thereby allowing custom CNNs to be trained for a specific application (image content and blur range). Additionally, we evaluate the behavior and limits of the CNNs with respect to blur direction range and length.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/7H14002" target="_blank" >7H14002: ALMARVI - Algorithms, Design Methods, and Many-Core Execution Platform for Low-Power Massive Data-Rate Video and Image Processing</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2016

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

IEEE International Conference on Image Processing (ICIP)

ISBN

978-1-4673-9961-6

ISSN

—

e-ISSN

—

Number of pages

4

Pages from-to

3832-3836

Publisher name

IEEE Signal Processing Society

Place of publication

Phoenix

Event location

Phoenix

Event date

Sep 25, 2016

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000390782003167