All-Focus Image Fusion and Depth Image Estimation Based on Iterative Splitting Technique for Multi-focus Images

This paper concerns about processing of multi-focus images which are captured by adjusting the positions of the imaging plane step by step so that objects at different depths will have their best focus at different images. Our goal is to synthesize an all-focus image and estimate the corresponding depth image for this multi-focus image set. In contrast to traditional pixel- or block-based techniques, our focus measures are computed based on irregular regions that are iteratively refined/split to adapt to varying image content. At first, an initial all-focus image is obtained and then segmented to get initial region definitions. The regional Focus Evaluation Curve (FEC) along the focal-length axis and a regional label histogram are then analyzed to determine whether a region should be subject to further splitting. After convergence, the final region definitions are used to perform WTA (Winner-take-all) for choosing image pixels of best focus from the image set. Depth image then corresponds to the label image by which image pixels of best focus are chosen. Experiments show that our adaptive region-based algorithm has performances (in synthesis quality, depth map, and speed) superior to other prior works and commercial software that adopt pixel-weighting strategy.