Decoding 1-D Barcode from Degraded Images Using a Neural Network

2011 ◽  
pp. 45-55
Author(s):  
A. Zamberletti ◽  
I. Gallo ◽  
M. Carullo ◽  
E. Binaghi
Keyword(s):  
Neural Network ◽  
Degraded Images

A Blur Classification Approach Using Deep Convolution Neural Network

2020 ◽  
Vol 11 (1) ◽  
pp. 93-111
Author(s):  
Shamik Tiwari
Keyword(s):  
Neural Network ◽  
Convolution Neural Network ◽  
Hand Gesture ◽  
Classification Approach ◽  
Degraded Image ◽  
Blind Restoration ◽  
Deep Convolution Neural Network ◽  
Simulation Results ◽  
Mlp Model ◽  
Degraded Images

Computer vision-based gesture identification is designed to recognize human actions with the help of images. During the process of gesture image acquisition, images suffer various degradations. The method of recovering these degraded images is called restoration. In the case of blind restoration of such a degraded image where blur information is unavailable, it is essential to determine the exact blur type. This article presents a convolution neural network model for blur classification which categories a blur found in a hand gesture image into one of the four blur categories: motion, defocus, Gaussian, and box blur. The simulation results demonstrate the improved preciseness of the CNN model when compared to the MLP model.

scholarly journals Assessing Neural Network Scene Classification from Degraded Images

2019 ◽  
Vol 16 (4) ◽  
pp. 1-20 ◽  
Author(s):  
Timothy Tadros ◽  
Nicholas C. Cullen ◽  
Michelle R. Greene ◽  
Emily A. Cooper
Keyword(s):  
Neural Network ◽  
Scene Classification ◽  
Degraded Images

Learning an Enhancement Convolutional Neural Network for Multi-degraded Images

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Ke Wang ◽  
Li Zhuo ◽  
Jiafeng Li ◽  
Tongyao Jia ◽  
Jing Zhang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Degraded Images

scholarly journals BLIND RESTORATION USING CONVOLUTION NEURAL NETWORK

2021 ◽  
Vol 1 (1) ◽  
pp. 25-32
Author(s):  
Meryem H. Muhson ◽  
Ayad A. Al-Ani
Keyword(s):  
Neural Network ◽  
Image Restoration ◽  
Super Resolution ◽  
Degraded Image ◽  
Blind Restoration ◽  
High Resolution Images ◽  
Image Super Resolution ◽  
Single Image Super Resolution ◽  
Blurred Images ◽  
Degraded Images

Image restoration is a branch of image processing that involves a mathematical deterioration and restoration model to restore an original image from a degraded image. This research aims to restore blurred images that have been corrupted by a known or unknown degradation function. Image restoration approaches can be classified into 2 groups based on degradation feature knowledge: blind and non-blind techniques. In our research, we adopt the type of blind algorithm. A deep learning method (SR) has been proposed for single image super-resolution. This approach can directly learn an end-to-end mapping between low-resolution images and high-resolution images. The mapping is expressed by a deep convolutional neural network (CNN). The proposed restoration system must overcome and deal with the challenges that the degraded images have unknown kernel blur, to deblur degraded images as an estimation from original images with a minimum rate of error.  

scholarly journals NEURAL NETWORK BASED CLASSIFICATION USING BLUR DEGRADATION AND AFFINE DEFORMATION INVARIANT FEATURES

2001 ◽  
Vol 10 (01n02) ◽  
pp. 243-256 ◽  
Author(s):  
YANI ZHANG ◽  
CHANGYUN WEN ◽  
YING ZHANG ◽  
YENG CHAI SOH
Keyword(s):  
Neural Network ◽  
Classification Accuracy ◽  
Pattern Analysis ◽  
Moment Invariants ◽  
Normalization Procedure ◽  
Image Normalization ◽  
Invariant Moments ◽  
High Classification Accuracy ◽  
Affine Deformation ◽  
Degraded Images

Identification of affine deformed and simultaneously blur degraded images is an important task in pattern analysis. In this paper, we introduce an image normalization approach to derive blur and affine combined moment invariants (BACIs). In our scheme, the lowest order blur invariant moments are used as the normalization constraints and an appropriate normalization procedure is designed to guarantee that the constraints used in each step should not be affected in the subsequent normalization steps. A neural network (NN) model is then employed to classify the degraded images using the proposed BACIs. Experimental results show that the system has high classification accuracy.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural
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