In area of white balance, the process of large colored background images seems to be a problem. Regarding this issue, a white balance algorithm based on facial skin color was proposed. A neural network-based object detection algorithm and an adaptive threshold segmentation algorithm were combined to achieve the accurate segmentation of skin color pixels. Then a 3-dimension color gamut mapping method in CIELAB color space was used to do the illumination estimation. Last, CAT16 model was applied to rendering the images to standard lighting condition. Besides, an ill white balanced images dataset taken against large colored backgrounds were prepared to test the present algorithm and others’ performance. The results show the proposed algorithm performs better on the dataset.
Contrast enhancement which is an important part of digital image processing has been studied for a long time and widely used in various fields such as digital photography or medical imaging. The purpose of contrast enhancement is to improve the overall contrast of the image and details on the local area. Contrast enhancement algorithms are classified into histogram based methods, tone mapping based methods, and retinex theory based methods. Particularly, retinex theory is widely applied at the spatial domain contrast enhancement. In this paper, we propose the contrast enhancement algorithm using the estimated illumination. Different from conventional retinex based algorithms, the estimated illumination serves as the tone mapping criterion and masked with original image. The intensity of estimated illumination image is adaptively modulated according to original image to improve the contrast of image effectively. Experimental results show that both global and local contrast are enhanced simultaneously with the proposed algorithm. Objective assessment using performance metrics also shows that the proposed method has the highest scores compared to the conventional methods.