Adaptive Region-Segmentation Multi-Focus Image Fusion Based on Differential Evolution

Abstract

An adaptive region-segmentation based multi-focus image fusion method is presented using a Laplacian pyramid transform which decomposes the pre-registered source images into approximate and detail coefficients. In order to avoid the disadvantage of fixed-size blocks, the adaptive differential evolution scheme is designed to compute the optimal-size block. Firstly, with approximate coefficients, the optimal-size blocks are iteratively calculated by an adaptive differential evolution algorithm. The initial decision diagram is then completed by comparing the regional sum-modified Laplacian energy of two corresponding blocks after the regional sum-modified Laplacian energy is calculated. Secondly, the initial decision diagram is optimized by the guided image filter to obtain the final decision diagram in order to avoid the block effect of boundary. With the decision diagram, the approximate coefficients are fused using the weighted mean rules, while the detail coefficients are fused using the regional gradient energy method. Finally, an inverse Laplacian pyramid transform is used to reconstruct the fused approximate coefficients and fused detail coefficients, and to acquire the fused image where all objects are clear. The experimental result proves that the proposed method produces fusion images of fewer artifacts or additional noise, with higher computational efficiency. The proposed method is also superior to the other state-of-the-art methods in both subjective visual effect and objective quantitative evaluation indicators.

A Survey of Multi-Focus Image Fusion Methods

“…Zhang et al [19] proposed a multi-focus image fusion method based on adaptive region segmentation, which decomposed pre-registered source images into approximate coefficients and detail coefficients using the Laplace pyramid transform. In order to avoid the defect of fixed block size, an adaptive differential evolution algorithm is designed to calculate the optimal block size.…”

“…Laplacian pyramid (LP) [9]; gradient pyramid (GD) [10]; contrast pyramid (CP) [11]; region mosaicking on Laplacian pyramids (RMLP) [12]; fast discrete curvelet transform (FDCT) [5]; dual-tree complex wavelet transform (DT-CWT) [13]; nonsubsampled contourlet transform (NSCT) [14]; nonsubsampled shearlet transform (NSST) [15]; cross sparse representation (CSP) [16]; independent component analysis (ICA) [17]; discrete cosine transform (DCT) [18] Boundary segmentation adaptive region-segmentation (ARS) [19]; morphology-based focus measure (MBFM) [20]; content adaptive blurring (CAB) [21]; robust principal component analysis (RPCA) [22]; Markov random field (MRF) [23]; optimal defocus estimation (ODE) [24] Deep learning convolutional neural networks (CNN) [25]; CNN based [26]; PCNN-Pulse Coupled Neural Network (PCNN) [27]; PCNN based [28]; Generative Adversarial Networks (GAN) [29] Combination CTD+SR [30]; NSCT+SR [31]; SF-PAPCNN+NSST+ ISML [32]; CNN+SR [33] 2…”

Exploring Types of Multi-Focus Image Fusion

“…Aslantas et al [12] utilized an optimization method to choose the block size, but the iterative procedures for optimization proved to be time-consuming. Additionally, alternative region-based image fusion algorithms [13] have been introduced, which involve splitting the source images into regions rather than blocks. These region-based algorithms begin by segmenting the source images using techniques such as normalized cuts [14], and then proceed to perform image fusion by measuring the clarity of corresponding regions and combining the sharply focused regions.…”

Decision Support System for Classification Medullary Thyroid Cancer

“…P (ϰ \ S) K-E where E assigns two function to detect the objects such colour and coherence. Threshold segmentation [20], [21], [22] creates two partitions of the objects based upon the high and low pixel values ranges from 0 to 255 energy levels. The pixels.…”