Renyi entropy and atom search sine cosine algorithm for multi focus image fusion

Abstract

This paper proposes a novel strategy, atom search sine cosine algorithm (ASSCA) for multi-focus image fusion. Here, the discrete wavelet transform (DWT) is adapted for transforming images into sub-bands. The fusion is carried out using a fusion rule based on weighting criteria that uses two attributes, Renyi entropy and the proposed ASSCA. Entropy discovers the entropy fusion factor considering the assessed entropy from the source image. Further, an optimization strategy, ASSCA is developed by integrating atom search optimization and sine cosine algorithm for precise selection of fusion factor. The output obtained from the fusion undergoes inverse discrete wavelet transform to obtain the resultant fused image. The proposed DWT + ASSCA + Renyi entropy outperformed other methods with maximal mutual information of 1.492, maximal peak signal-to-noise ratio of 40.625 dB, minimal root mean-squared error of 7.651, maximum correlation coefficient of 0.988, and minimum deviation index of 1.146.