Correcting Multi-focus Images via Simple Standard Deviation for Image Fusion
This addresses image fusion for multi-focus images in image processing, but it is incremental as it applies a standard statistical technique to a known bottleneck.
The paper tackled the problem of fusing multi-focus images by using simple statistical standard deviation to identify focused regions based on pixel dispersion, resulting in very satisfactory visual outcomes despite the method's simplicity.
Image fusion is one of the recent trends in image registration which is an essential field of image processing. The basic principle of this paper is to fuse multi-focus images using simple statistical standard deviation. Firstly, the simple standard deviation for the k-by-k window inside each of the multi-focus images was computed. The contribution in this paper came from the idea that the focused part inside an image had high details rather than the unfocused part. Hence, the dispersion between pixels inside the focused part is higher than the dispersion inside the unfocused part. Secondly, a simple comparison between the standard deviation for each k-by-k window in the multi-focus images could be computed. The highest standard deviation between all the computed standard deviations for the multi-focus images could be treated as the optimal that is to be placed in the fused image. The experimental visual results show that the proposed method produces very satisfactory results in spite of its simplicity.