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This paper explores an image fusion algorithm based on self-adaptive fuzzy clustering algorithm. The clustering method combined nearest neighbor clustering method with k-means clustering method (NNKM) is adopted in pixel classification. The membership of every image pixel to each cluster center is introduced. And the fused image membership obtained by maximum rule is adopted as the weighting coefficient of the weighted mean strategy, which is used to obtain the fusion image. In this experiment, the fusion results of various Radiuses of Gauss function are compared with the results obtained by nearest neighbor clustering method. The experiment results show that the proposed method can achieve better performance of the fused image.

Caused by light reflected off the subject's retina, red-eye is a troublesome problem in consumer photography. Although most of the cameras have the red-eye reduction mode, the result reality is that no on-camera system is completely effective. In this paper, we propose a fully automatic approach to detecting and correcting red-eyes in digital images. In order to detect red-eyes in a picture, a heuristic yet efficient algorithm is first adopted to detect a group of candidate red regions and then an eye classifier is utilized to confirm whether each candidate region is a human eye. Thereafter, for each detected redeye, we can correct it by the correction algorithm. In case that a red-eye cannot be detected automatically, another algorithm is also provided to detect red-eyes manually with the user's interaction by clicking on an eye. Experimental results on about 300 images with various red-eye appearances demonstrate that the proposed solution is robust and effective.