A Review on Various Methods for Classification of Massive Noisy Image

In image processing there are significant research areas to work on the image but classification of image is one of the most complex area. If the image contains blurred and noisy content, it will make the image classification process as a more complex and challenging task. Irrespective of the tediousness of such image, there are still various techniques to classify the images. If the input image contains high amount of noisy content and blurriness level, then these methods shows some limitations. The image classification results are not satisfactory and the accuracy of the methods are low. Generally, the classification methods are categorized in two main types, they are supervised and unsupervised classification. Both the methods have their own advantages as well as limitations. The main challenge is to obtain a proper and desired result from a noisy image as compared to that of the normal image. In this study we present an analytical review on various image classification techniques.