BibTeX:

@article{IJIRSTV6I2013,
     title={Review on: Performance Analysis of LRMR and SVM for Image Restoration},
     author={Harmeet Kour and Manmeen Kaur},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={6},
     number={2},
     pages={5--7},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV6I2013.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

In image restoration noise is removed from image to obtain the original image. There are many ways to process the images such as optical, photographic or electronic way to process the images but the use of digital computers in image processing is fast and convenient way to process the images. Image Restoration is a process in which high quality images are obtained from degraded images. In this process distorted images are recovered to their original state. In this paper, Restoration of the degraded image is done using various advance restoration techniques such as low rank matrix recovery(LRMR) and support vector machine(SVM).In case of normal biogeography and then examine how it can be used to improve various problems related to degraded images. We observe that SVM has various features such as classifier in the same manner as other biology-based optimization methods such as GAs and particle swarm optimization (PSO). This makes SVM appropriate to many of the same types of issues that GAs and PSO are used for specifically and high-dimension problems with multiple local optima.

Image Restoration, Low Rank Matrix Recovery (LRMR), Support Vector Machine (SVM) particle swarm optimization (PSO)