A fast learning-based super-resolution method for copper strip defect image

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740042 ◽  
Author(s):  
Zhuo Zhang ◽  
Xinnan Fan ◽  
Xuewu Zhang
Keyword(s):  
Detection System ◽  
Super Resolution ◽  
Textural Features ◽  
Resolution Method ◽  
Copper Strip ◽  
Fast Learning ◽  
Efficiency And Effectiveness ◽  
Resolution Model ◽  
Defect Image ◽  
Weak Target

In this paper, a fast pre-classified-based super-resolution model has been proposed to overcome the problems of degraded imaging in weak-target real-time detection system, specialized to copper defect detection. To accurately characterize the defected image, textural features based on the statistical function of gray-gradient are presented. Furthermore, to improve the effectiveness and practicality of the online detection, a concept of pre-classified learning is introduced and an edge smoothness rule is designed. Some experiments are carried out on defect images in different environments and the experimental results show the efficiency and effectiveness of the algorithm.

Simulation of the Mechanism of Insect Bionic Compound Eye Super-Resolution Restoration Technology of Copper Strip Surface Defect Images

2013 ◽  
Vol 462-463 ◽  
pp. 302-307
Author(s):  
Xiao Dan Sun ◽  
Xin Nan Fan ◽  
Ming Qiang Ling ◽  
Hong Hai Zhuang ◽  
Zhuo Zhang ◽  
...  
Keyword(s):  
Surface Defect ◽  
Compound Eye ◽  
Super Resolution ◽  
Reconstruction Technique ◽  
Copper Strip ◽  
Strip Surface ◽  
Imaging Technology ◽  
Inspection Process ◽  
Defect Image ◽  
Surface Defect Detection

In view of the problem that in traditional copper strip surface defect inspection process, the low resolution of the collected images will greatly decrease the accuracy of the detect defects, through the study of biological bionic imaging technology, combined with insect bionic compound eye imaging technology, this paper proposes a visual simulation insects bionic mechanism of the copper strip surface defect image super-resolution reconstruction technique. Through the study of biological bionic imaging technology, and take the advantage of insect compound eye visual imaging mechanism, this paper uses multiple linear array CCD image sensors to collect images aimed at getting defect images in all perspectives. The actual input images are restored by the method of super-resolution reconstruction using the sample library to improve the resolution of the image. Through the large amount of experiments of different copper strip surface defect images, and then compare the results, it can be seen that taking this papers method to dispose the defect images, will improve the images PSNR value, and has greatly improved the images quality, which will do good to improve the accuracy of the copper strip surface defect detection.

Contribution of enhanced DWT and non-negative structured sparse representation for image super resolution: A performance analysis

2020 ◽  
Vol 18 (06) ◽  
pp. 2050049
Author(s):  
A. Valli Bhasha ◽  
B. D. Venkatramana Reddy
Keyword(s):  
Sparse Representation ◽  
Hyperspectral Image ◽  
Super Resolution ◽  
Image Resolution ◽  
Discrete Wavelet ◽  
Spotted Hyena ◽  
Resolution Model ◽  
Deep Cnn ◽  
Image Super Resolution ◽  
Two Phases

The image super-resolution methods with deep learning using Convolutional Neural Network (CNN) have been producing admirable advancements. The proposed image resolution model involves the following two main analyses: (i) analysis using Adaptive Discrete Wavelet Transform (ADWT) with Deep CNN and (ii) analysis using Non-negative Structured Sparse Representation (NSSR). The technique termed as NSSR is used to recover the high-resolution (HR) images from the low-resolution (LR) images. The experimental evaluation involves two phases: Training and Testing. In the training phase, the information regarding the residual images of the dataset are trained using the optimized Deep CNN. On the other hand, the testing phase helps to generate the super resolution image using the HR wavelet subbands (HRSB) and residual images. As the main novelty, the filter coefficients of DWT are optimized by the hybrid Fire Fly-based Spotted Hyena Optimization (FF-SHO) to develop ADWT. Finally, a valuable performance evaluation on the two benchmark hyperspectral image datasets confirms the effectiveness of the proposed model over the existing algorithms.

Study of advanced mask inspection optics with super-resolution method for next-generation mask fabrication

2009 ◽  
Author(s):  
Ryoichi Hirano ◽  
Masatoshi Hirono ◽  
Riki Ogawa ◽  
Nobutaka Kikuiri ◽  
Kenichi Takahara ◽  
...  
Keyword(s):  
Super Resolution ◽  
Next Generation ◽  
Resolution Method ◽  
Mask Inspection ◽  
Mask Fabrication
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