A novel multi-focus image fusion algorithm based on feature extraction and wavelets

Focusing cameras is an important problem in computer vision and microscopy. Due to the limited depth of field of optical lenses in CCD devices, there are sensors which cannot generate images of all objects with equal sharpness. Therefore, several images of the same scene have different focused parts. One way to overcome this problem is to take different in-focus parts and combine them into a single composite image which contains the entire focuused scene. In this paper we present a multi-focus image fusion algorithm based on feature extraction and wavelets. Classical wavelet synthesis is known to produce Gibbs phenomenon around discontinuities. The approach of wavelet on the interval transform is suitable to orthogonal wavelets and does not exhibit edge effects. Since canny filter's operator is a Gaussian derivative, a well known model of early vision, we used it to get salient edges and to build a decision map who determines which information to take and at what place. Finally, quality of fused images is assessed using both traditional and perception-based quality metrics. Quantitative and qualitative analysis of the results demonstrate higher performance of the algorithm compared to traditional methods.