Focusing on the problems of low quality of obtained images and low algorithm efficiency of existing multi-exposure image fusion algorithms, a multi-exposure image fusion algorithm based on local features of scene was proposed. Firstly, the image sequence with different exposures was divided into regular patches with overlapping regions of some pixels between neighbouring patches. For static scenes, the weight for each patch was calculated based on local variance, local visibility and local saliency; for dynamic scenes, in addition to the three features described above, local similarity was also used to remove ghost caused by moving objects. Then, the optimal patches were obtained based on the weighted sum method. Finally, the output patches were fused together and the pixels in overlapping regions were averaged to obtain the final fusion result. With 12 sets of exposure sequences of different natural scenes, the proposed algorithm was compared with 7 existing pixel-based and feature-based algorithms in subjective and objective aspects. Experimental results demonstrate that the proposed algorithm preserves more details and obtains good visual effects in both static scenes and dynamic scenes. At the same time, the proposed algorithm also maintains high computational efficiency.

To solve the problem of information loss and obvious block effect in multi-focus image fusion, a novel multi-focus image fusion method based on image matting technique was proposed. Firstly, the rough focus information of each source image was obtained by focusing measure, all of which was used to generate the trimap of the fusion image, namely, foreground, background and unknown region. Secondly, according to the trimap, the precise focus region of each source image could be gotten by using the image matting. Finally, the obtained focus regions were combined to consist of new foreground and background. And the optimal fusion of the unknown region was conducted on the basis of the foreground and background, which enhanced the correlations between the nearby pixels of the three focusing regions. The experimental results show that compared with the traditional algorithms, the proposed algorithm can acquire higher Mutual Information (MI) and edge preservation on the objective evaluation. For the subjective evaluation, it can be seen that the block effect is obviously suppressed and the visual effect is more excellent. The proposed algorithm can be applied to the object identification and computer vision to obtain optimal fusion result.