Adaptive Fuzzy Exposure Local Contrast Enhancement

Numerous factors, such as illumination condition, can affect image quality. Local contrast enhancement is an approach for improving the local visibility detail of an image by increasing the contrast in local regions. Recently, researchers have shown an interest in solving the issue of non-uniform illumination. However, all the studies failed to improve the contrast of the non-uniform illumination and low-contrast images locally and regionally by individually focusing on each region and improving its contrast. The contrast of each region will be enhanced using a new local contrast enhancement technique called adaptive fuzzy exposure local contrast enhancement (AFELCE). The proposed AFELCE method is specifically designed to enhance the contrast by using specific algorithms for different regions. The proposed AFELCE technique successfully improves the contrast of 300 low-contrast and non-uniform illumination images taken from three different databases, namely, standard, underwater (UW), and microscopic human sperm (MHS) images. The proposed AFELCE method outperforms the state-of-the-art methods in terms of quality and quantity. Qualitatively, the proposed AFELCE method has successfully enhanced the contrast of the images by producing more uniform illumination images with high contrast than the other methods. Quantitatively, the proposed AFELCE method produces the highest average of entropy (E), measurement of enhancement (EME), and universal image quality index (UIQI) for the standard image database with the values of 7.582, 42.75, and 0.94, respectively. Similar results are obtained for the UW image database, where the proposed method produces the highest average of E, EME, and UIQI values with 7.124, 41.13, and 0.89, respectively. For the MHS image database, AFELCE produces the highest values for E and EME with the values of 7.602 and 42.51, respectively.