Illumination estimation algorithms are aimed to estimate the RGB of scene illumination color when the image was taken, which is a significant way to achieve color constancy. They can be divided into three categories: pixel-based algorithms, learning-based algorithms and combination algorithms. Compared with other two kinds of illumination estimation algorithms, pixel-based algorithms are relatively poorly performing. In this paper, we add a L<sub>0</sub>-norm smoothing preprocessing to pixel-based algorithms to improve the performance. The L0-norm smoothing can suppress insignificant details and maintain major edges of an image. Experimental results show that our optimization approach is effective to enhance the performance of pixel-based algorithms.
Illumination estimation is an important part of color constancy. It is known that estimating the scene illumination color is an ill-posed problem, and no method can be considered as universal. To select an optimal method for a particular image or scene, some combination methods based on characteristic similarity were proposed. These methods found that the images having similar characteristics can use the same method as an optimal technique. Although the combination methods based on image characteristics have worked well in some scenes, the accuracy of these methods is limited to the accuracy of the set of the unitary method. However, we assume that images with similar characteristics have similar scene illumination color and propose an illumination estimation method which is based on image characteristics. According to the characteristics of each image, we search K images whose characteristics are similar to the image from the image dataset, and the standard illumination color of the K selected images is known. Then, we use the weighted average method to estimate the new image’s illumination color by combining the standard illumination color of the K selected images. The experimental results show that the proposed method outperforms other state-of-the-art methods.