Multi-Focus Image Fusion With Point Detection Filter and Superpixel-Based Consistency Verification

An accurate and efficient measurement of pixel's sharpness is a critical factor in most multi-focus image fusion methods. In our practice, we found that the focused regions get more blurred than the defocus regions when the multi-focus images are blurred digitally. Based on this observation, a novel multi-focus image fusion method is presented in this paper. In the given fusion scheme, focused regions detection is achieved by point detection filter and Gaussian filter, which has been certified more effective than other frequently used image clarity measures. Moreover, unlike the other commonly used consistency verification, we propose a superpixel-based consistency verification (SCV) method by integrating the image superpixels to improve the fusion performance. Image superpixels can perceptually represent meaningful image local features. Two datasets of multi-focus images are used to conduct experiments. Experimental results demonstrate that the proposed method can be competitive with or even outperform the state-of-the-art fusion methods in terms of both subjective visual perception and objective evaluation metrics.