[Show abstract][Hide abstract] ABSTRACT: There are several images that do not have uniform brightness which pose a
challenging problem for image enhancement systems. As histogram equalization
has been successfully used to correct for uniform brightness problems, a
histogram equalization method that utilizes human visual system based
thresholding(human vision thresholding) as well as logarithmic processing
techniques were introduced later . But these methods are not good for
preserving the local content of the image which is a major factor for various
images like medical and aerial images. Therefore new method is proposed here.
This method is referred as "Human vision thresholding with enhancement
technique for dark blurred images for local content preservation". It uses
human vision thresholding together with an existing enhancement method for dark
blurred images. Furthermore a comparative study with another method for local
content preservation is done which is further extended to make it suitable for
contrast improvement. Experimental results shows that the proposed methods
outperforms the former existing methods in preserving the local content for
standard images, medical and aerial images.
[Show abstract][Hide abstract] ABSTRACT: Adaptive (local)histogram equalization is a popular and effective algorithm for local content emphasis. But it has a disadvantage of the introduction and amplification of speckle noise. This in turn leads to the loss of information. So an extension of the method called multiple layers block overlapped histogram equalization for local content emphasis is done here which is referred to as Multilayered Contrast Limited Adaptive Histogram Equalization Using Frost Filter which mainly focus on the application to medical images. The proposed method uses frost filter for the speckle noise reduction. Unlike the former method, the proposed method uses the combination of frost filter and median filter on CLAHE(Contrast limited adaptive histogram equalization) images. Here a comparative study of the application of the former method on CLAHE images and the proposed method on CLAHE images is done. Experimental results show that the latter gives better results compared to the former.
Recent Advances in Intelligent Computational Systems (RAICS), 2011 IEEE; 01/2011