Soft computing-based non-linear discriminate classifier for multimedia image quality enhancement

Author(s):  
D. Ferlin Deva Shahila ◽  
S. H. Krishnaveni ◽  
Valantina Stephen
2009 ◽  
Vol 129 (6) ◽  
pp. 593-600 ◽  
Author(s):  
Yuichiro Tokuda ◽  
Gosuke Ohashi ◽  
Masato Tsukada ◽  
Reiichi Kobayashi ◽  
Yoshifumi Shimodaira

2019 ◽  
Vol 2019 (1) ◽  
pp. 360-368
Author(s):  
Mekides Assefa Abebe ◽  
Jon Yngve Hardeberg

Different whiteboard image degradations highly reduce the legibility of pen-stroke content as well as the overall quality of the images. Consequently, different researchers addressed the problem through different image enhancement techniques. Most of the state-of-the-art approaches applied common image processing techniques such as background foreground segmentation, text extraction, contrast and color enhancements and white balancing. However, such types of conventional enhancement methods are incapable of recovering severely degraded pen-stroke contents and produce artifacts in the presence of complex pen-stroke illustrations. In order to surmount such problems, the authors have proposed a deep learning based solution. They have contributed a new whiteboard image data set and adopted two deep convolutional neural network architectures for whiteboard image quality enhancement applications. Their different evaluations of the trained models demonstrated their superior performances over the conventional methods.


2021 ◽  
Vol 15 ◽  
pp. 174830262110080
Author(s):  
Changjun Zha* ◽  
Qian Zhang* ◽  
Huimin Duan

Traditional single-pixel imaging systems are aimed mainly at relatively static or slowly changing targets. When there is relative motion between the imaging system and the target, sizable deviations between the measurement values and the real values can occur and result in poor image quality of the reconstructed target. To solve this problem, a novel dynamic compressive imaging system is proposed. In this system, a single-column digital micro-mirror device is used to modulate the target image, and the compressive measurement values are obtained for each column of the image. Based on analysis of the measurement values, a new recovery model of dynamic compressive imaging is given. Differing from traditional reconstruction results, the measurement values of any column of vectors in the target image can be used to reconstruct the vectors of two adjacent columns at the same time. Contingent upon characteristics of the results, a method of image quality enhancement based on an overlapping average algorithm is proposed. Simulation experiments and analysis show that the proposed dynamic compressive imaging can effectively reconstruct the target image; and that when the moving speed of the system changes within a certain range, the system reconstructs a better original image. The system overcomes the impact of dynamically changing speeds, and affords significantly better performance than traditional compressive imaging.


2018 ◽  
Vol 15 (4) ◽  
pp. 045204 ◽  
Author(s):  
Xiaohui Shi ◽  
Xianwei Huang ◽  
Suqin Nan ◽  
Hengxing Li ◽  
Yanfeng Bai ◽  
...  

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