Histogram equalization is the most popular method for image enhancement. However it has some drawbacks: i) it causes undesirable artifacts and ii) it can degrade the visual quality. To overcome the drawbacks, in this letter, multi-histogram equalization on smoothed histogram using a Gaussian kernel is proposed. To demonstrate the effectiveness, the method is tested on several images and compared with conventional methods.

An approach to image thresholding based on the plausibility of object and background regions by adopting a co-occurrence matrix and category utility is presented. The effectiveness of the proposed method is shown through the experimental results tested on several images and compared with conventional methods.

We propose a thresholding method based on interval-valued fuzzy sets which are used to define the grade of a gray level belonging to one of the two classes, an object and the background of an image. The effectiveness of the proposed method is demonstrated by comparing our classification results on eight test images to results from the conventional methods.

The thresholding results for gray level images depend greatly on the thresholding method applied. However, this letter proposes a histogram equalization-based thresholding algorithm that makes the thresholding results insensitive to the thresholding method applied. Experimental results are presented to demonstrate the effectiveness of the proposed thresholding algorithm.

We propose a simple but effective image segmentation method not based on thresholding but on a merging strategy by evaluating joint probability of gray levels on co-occurrence matrix. The effectiveness of the proposed method is shown through a segmentation experiment.