Research on Fast Enhancement of Motion Blurred Image Based on Improved Deep Learning

PurposeBook sorting system is one of specific application in smart library scenarios, and it now has been widely used in most libraries based on RFID (radio-frequency identification devices) technology. Book identification processing is one of the core parts of a book sorting system, and the efficiency and accuracy of book identification are extremely critical to all libraries. In this paper, the authors propose a new image recognition method to identify books in libraries based on barcode decoding together with deep learning optical character recognition (OCR) and describe its application in library book identification processing.Design/methodology/approachThe identification process relies on recognition of the images or videos of the book cover moving on a conveyor belt. Barcode is printed on or attached to the surface of each book. Deep learning OCR program is applied to improve the accuracy of recognition, especially when the barcode is blurred or faded. The approach the authors proposed is robust with high accuracy and good performance, even though input pictures are not in high resolution and the book covers are not always vertical.FindingsThe proposed method with deep learning OCR achieves best accuracy in different vertical, skewed and blurred image conditions.Research limitations/implicationsMethods that the authors proposed need to cooperate and practice in different book sorting machine.Social implicationsThe authors collected more than 500 books from a library. These photos display the cover of more than 100 randomly picked books with backgrounds in different colors, each of which has about five different pictures captured from variety angles. The proposed method combines traditional barcode identification algorithm with the authors’ modification to locate and deskew the image. And deep learning OCR is involved to enhance the accuracy when the barcode is blurred or partly faded. Book sorting system design based on this method will also be introduced.Originality/valueExperiment demonstrates that the accuracy of the proposed method is high in real-time test and achieves good accuracy even when the barcode is blurred. Deep learning is very effective in analyzing image content, and a corresponding series of methods have been formed in video content understanding, which can be a greater advantage and play a role in the application scene of intelligent library.

Download Full-text

Increasing the Image Sharpness of Light Microscope Images Using Deep Learning

Practical Metallography ◽

10.1515/pm-2021-0061 ◽

2021 ◽

Vol 58 (11) ◽

pp. 684-696

Author(s):

P. Krawczyk ◽

A. Jansche ◽

T. Bernthaler ◽

G. Schneider

Keyword(s):

Deep Learning ◽

Light Microscope ◽

Large Scale ◽

Lithium Ion ◽

Training Data ◽

Image Sharpness ◽

Microscope Images ◽

Blurred Image ◽

Cast Alloys ◽

Blurred Images

Abstract Image-based qualitative and quantitative structural analyses using high-resolution light microscopy are integral parts of the materialographic work on materials and components. Vibrations or defocusing often result in blurred image areas, especially in large-scale micrographs and at high magnifications. As the robustness of the image-processing analysis methods is highly dependent on the image grade, the image quality directly affects the quantitative structural analysis. We present a deep learning model which, when using appropriate training data, is capable of increasing the image sharpness of light microscope images. We show that a sharpness correction for blurred images can successfully be performed using deep learning, taking the examples of steels with a bainitic microstructure, non-metallic inclusions in the context of steel purity degree analyses, aluminumsilicon cast alloys, sintered magnets, and lithium-ion batteries. We furthermore examine whether geometric accuracy is ensured in the artificially resharpened images.

Download Full-text

Deep Learning

10.1017/cbo9780511780295 ◽

2009 ◽

Cited By ~ 94

Author(s):

Stellan Ohlsson

Keyword(s):

Deep Learning

Download Full-text

Intelligence artificielle : le futur de l’Orthodontie ?

Revue d Orthopédie Dento-Faciale ◽

10.1051/odf/2019026 ◽

2019 ◽

Vol 53 (3) ◽

pp. 281-294

Author(s):

Jean-Michel Foucart ◽

Augustin Chavanne ◽

Jérôme Bourriau

Keyword(s):

Big Data ◽

Deep Learning ◽

Intelligence Artificielle ◽

Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

Download Full-text