Author:
Noor Ameen A.,Abood Ziad M.,Mahmood Ali Shakir
Abstract
It has been relied upon and is still found in the fields of scientific research, especially astronomy, medicine (for accurate disease diagnosis), biology, archeology, and industry on video and still images. The low accuracy and quality of some videos are often due to a poor lens type or angle, which may be due to a lack of photographic experience, or because of the older sections, which can be affected by the coolness of the surrounding perimeter. This research was completed using simple methods of processing based on using a program to convert video to individual images, then a number of image processing operations to improve quality, and finally re-assemble the images to the video more accurately than the original and in our own way. The proposed process consists of several steps: cutting the video clip into a set of images, performing various operations, such as using the contrast filter first, discovering the edges, smoothing the image, and improving image density prior to assembly. We finally assemble the images back into clips. This has been the process we used on many of the films affected by noise, or damaged for a long time, and has proven our ability to improve the quality of the video.
Publisher
Southwest Jiaotong University
Reference20 articles.
1. WANG, L. (2010) An Algorithm for Repairing Low-Quality Video Enhancement Techniques Based on Trained Filter. Master thesis. [Online] Available from: https://arxiv.org/ftp/arxiv/papers/1103/1103.0540.pdf [Accessed 28/11/19].
2. DONG, X., WEN, J., LI, W., PANG, Y., and WANG, G. (2011) An efficient and integrated algorithm for video enhancement in challenging lighting conditions. [Online] Available from: https://arxiv.org/pdf/1102.3328.pdf [Accessed 28/11/19].
3. RAO, Y., CHEN, Z., SUN, M.-T., HSU, Y.-F., and ZHANG, Z. (2011) An effecive night video enhancement algorithm. In: Proceedings of the 2011 Visual Communications and Image Processing, Tainan, November 2011. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers.
4. YADAV, G., MAHESHWARI, S., and AGARWAL, A. (2014) Contrast limited adaptive histogram equalization based enhancement for real time video system. In: Proceedings of the 2014 International Conference on Advances in Computing, Communications and Informatics, New Delhi, September 2014. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers.
5. MAHMOOD, M., AL-KUBAISY, W.J., and AL-KHATEEB, B. (2019) Using Artificial Neural Network for Multimedia Information Retrieval. Journal of Southwest Jiaotong University, 54 (3). Available from http://jsju.org/index.php/journal/article/view/296.