An Image Enhancement Framework for Fault Diagnosis and Feature Extraction

Image enhancement refers to accentuation, or sharpening, of image features such as edges, boundaries, or contrast to make a graphic display more useful for display and analysis. The enhancement process does not increase the inherent information content in the data. But it does increase the dynamic range of the chosen features so that they can be detected easily. The greatest difficulty in image enhancement is quantifying the criterion for enhancement. Therefore, a large number of image enhancement techniques are empirical and require interactive procedures to obtain satisfactory results. Many image enhancement techniques are based on spatial operations performed on local neighborhoods of input pixels. Often, the image is convolved with a finite impulse response filter called spatial mask. This study will highlight various image enhancement techniques along with their benchmark results. Image enhancement technologies have attracted much attention during the diagnosis process. The principle objective of image enhancement techniques is to process an input image so that the resultant image is more suitable than the original image for specific application. Traditional global histogram equalization usually causes excessive contrast. Enhancement while local histogram equalization may cause block effect. To overcome these problems, a new method for image contrast enhancement is developed. The novelty of the proposed method is that the weighted average of the histogram equalized, gamma corrected and the original image are combined to obtained the enhanced processed image. The proposed algorithm not only achieve contrast enhancement but also preserves the brightness level. Experimental results show that the proposed algorithm has good performance on enhancing contrast and visibility for a majority of images.