Super Resolution for e-Learning
Date Issued
2011
Date
2011
Author(s)
Cheng, Yi-Chi
Abstract
Because of the development of image display devices, people require much better quality of images. The high-level image capture devices are still expensive, therefore we usually use software to enhance the quality of images. Super- resolution algorithm is a popular research domain in digital image processing, and the applications are widespread, including the military, surveillance, and so on.
Recently, the requirement of on-line digital learning is much higher, but many teachers do not have good image capture devices, they cannot record teaching contents with high quality. In this paper, we propose a method to enhance the normal teaching images, especially for the teaching mode using black board, white board, or projection screen.
Though super-resolution algorithm can enhance the image resolution, the large execution time and disregarding the image content are the problems in majority of super-resolution algorithm. In this paper, we use edge detection to estimate the image edge density of small blocks, and use mean shift to implement color segmentation. We can integrate the above information to determine where people pay attention to mostly, where secondly, and where we do not care, and use different complexity algorithms to process them.
By the experiment result, we only have to process 20% area of the whole image, and decrease execution time significantly. Simultaneously, we can only get an image output with good quality.
Subjects
Super resolution
e-Learning
Type
thesis
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