Banding defect detection and image quality classification

2021 ◽  
Vol 2021 (16) ◽  
pp. 245-1-245-9
Author(s):  
Yi Yang ◽  
Eric Maggard ◽  
Yousun Bang ◽  
Minki Cho ◽  
Jan P. Allebach
Keyword(s):  
Image Quality ◽  
Defect Detection ◽  
Printing Industry ◽  
Specific Test ◽  
Processing Pipeline ◽  
Quality Classification ◽  
Perceptual Assessment ◽  
Periodic Interval

Banding has been regarded as one of the most severe defects affecting the overall image quality in the printing industry. There has been a lot of research on it, but most of them focused on uniform pages or specific test images. Aiming at detecting banding on customer’s content pages, this paper proposes a banding processing pipeline that can automatically detect banding, identify periodic and isolated banding, and estimate the periodic interval. In addition, based on the detected banding characteristics, the pipeline predicts the overall quality of printed customer’s content pages and obtains predictions similar to human perceptual assessment.

scholarly journals Automatic Defect Detection for Web Offset Printing Based on Machine Vision

2019 ◽  
Vol 9 (17) ◽  
pp. 3598 ◽  
Author(s):  
Erhu Zhang ◽  
Yajun Chen ◽  
Min Gao ◽  
Jinghong Duan ◽  
Cuining Jing
Keyword(s):  
Image Registration ◽  
Defect Detection ◽  
Feature Matching ◽  
Reference Image ◽  
Printing Industry ◽  
Offset Printing ◽  
Shape Context ◽  
Image Subtraction ◽  
Registration Method

In the printing industry, defect detection is of crucial importance for ensuring the quality of printed matter. However, rarely has research been conducted for web offset printing. In this paper, we propose an automatic defect detection method for web offset printing, which consists of determining first row of captured images, image registration and defect detection. Determining the first row of captured images is a particular problem of web offset printing, which has not been studied before. To solve this problem, a fast computational algorithm based on image projection is given, which can convert 2D image searching into 1D feature matching. For image registration, a shape context descriptor is constructed by considering the shape concave-convex feature, which can effectively reduce the dimension of features compared with the traditional image registration method. To tolerate the position difference and brightness deviation between the detected image and the reference image, a modified image subtraction is proposed for defect detection. The experimental results demonstrate the effectiveness of the proposed method.

Observability of Very Thick Specimen by Using Transmission Scanning Electron Microscope

1971 ◽  
Vol 29 ◽  
pp. 26-27
Author(s):  
K. Shibatomi ◽  
T. Yamanoto ◽  
H. Koike
Keyword(s):  
Electron Microscope ◽  
Image Quality ◽  
Scanning Electron Microscope ◽  
Chromatic Aberration ◽  
Image Contrast ◽  
Objective Lens ◽  
Thick Specimen ◽  
Transmission Electron ◽  
Scanning Electron

In the observation of a thick specimen by means of a transmission electron microscope, the intensity of electrons passing through the objective lens aperture is greatly reduced. So that the image is almost invisible. In addition to this fact, it have been reported that a chromatic aberration causes the deterioration of the image contrast rather than that of the resolution. The scanning electron microscope is, however, capable of electrically amplifying the signal of the decreasing intensity, and also free from a chromatic aberration so that the deterioration of the image contrast due to the aberration can be prevented. The electrical improvement of the image quality can be carried out by using the fascionating features of the SEM, that is, the amplification of a weak in-put signal forming the image and the descriminating action of the heigh level signal of the background. This paper reports some of the experimental results about the thickness dependence of the observability and quality of the image in the case of the transmission SEM.

Organ doses and subjective image quality of indirect digital panoramic radiography

2001 ◽  
Vol 30 (6) ◽  
pp. 308-313 ◽  
Author(s):  
F Gijbels ◽  
G Sanderink ◽  
C Bou Serhal ◽  
H Pauwels ◽  
R Jacobs
Keyword(s):  
Image Quality ◽  
Panoramic Radiography ◽  
Organ Doses ◽  
Subjective Image Quality ◽  
Digital Panoramic Radiography

Image Quality Enhancing by Efficient Histogram Equalization

2014 ◽  
Vol 2 (2) ◽  
pp. 47-58
Author(s):  
Ismail Sh. Baqer
Keyword(s):  
Image Quality ◽  
Contrast Enhancement ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Gray Level ◽  
Signal To Noise ◽  
Squared Error ◽  
Noise Ratio

A two Level Image Quality enhancement is proposed in this paper. In the first level, Dualistic Sub-Image Histogram Equalization DSIHE method decomposes the original image into two sub-images based on median of original images. The second level deals with spikes shaped noise that may appear in the image after processing. We presents three methods of image enhancement GHE, LHE and proposed DSIHE that improve the visual quality of images. A comparative calculations is being carried out on above mentioned techniques to examine objective and subjective image quality parameters e.g. Peak Signal-to-Noise Ratio PSNR values, entropy H and mean squared error MSE to measure the quality of gray scale enhanced images. For handling gray-level images, convenient Histogram Equalization methods e.g. GHE and LHE tend to change the mean brightness of an image to middle level of the gray-level range limiting their appropriateness for contrast enhancement in consumer electronics such as TV monitors. The DSIHE methods seem to overcome this disadvantage as they tend to preserve both, the brightness and contrast enhancement. Experimental results show that the proposed technique gives better results in terms of Discrete Entropy, Signal to Noise ratio and Mean Squared Error values than the Global and Local histogram-based equalization methods

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