A Survey of Multi-Focus Image Fusion Methods

Abstract

As an important branch in the field of image fusion, the multi-focus image fusion technique can effectively solve the problem of optical lens depth of field, making two or more partially focused images fuse into a fully focused image. In this paper, the methods based on boundary segmentation was put forward as a group of image fusion method. Thus, a novel classification method of image fusion algorithms is proposed: transform domain methods, boundary segmentation methods, deep learning methods, and combination fusion methods. In addition, the subjective and objective evaluation standards are listed, and eight common objective evaluation indicators are described in detail. On the basis of lots of literature, this paper compares and summarizes various representative methods. At the end of this paper, some main limitations in current research are discussed, and the future development of multi-focus image fusion is prospected.

Multi-Focus Microscopy Image Fusion Based on Swin Transformer Architecture

“…Traditional MFIF methods are typically categorized into two main groups: transform domain methods and spatial domain methods [33]. Transform domain methods mainly operate the decomposition coefficient after image transformation, encompassing three key fusion stages: image transformation, coefficient after image transformation, and inverse transformation reconstruction [34]. According to the application of the image transform, transform domain methods can be further classified into multi-scale decomposition (MSD)-based methods (e.g., Laplacian pyramid [14,35], discrete wavelet transform [36,37], nonsubsampled contourlet transform [38][39][40], neighbor distance filtering [41,42], empirical mode decomposition [43]), sparse representation (SR)-based methods (orthogonal matching pursuit [44,45]), gradient domain (GD)-based methods (structure tensor [46,47]), methods based on other transform (independent component analysis [48], cartoon-texture decomposition [49]) and methods based on the combination of different transforms (curvelet transform and wallet transform [50]).…”

A State-of-the-Art Review of Non-Destructive Testing Image Fusion and Critical Insights on the Inspection of Aerospace Composites towards Sustainable Maintenance Repair Operations

“…Shifted and scaled versions of a wavelet can positively impact the resolution in the time or frequency domain according to the selected parameters. The wavelet transform aims to maintain a good resolution in the time and frequency domain, contrary to the Fourier transform [73,74].…”

Fusion Insights from Ultrasonic and Thermographic Inspections for Impact Damage Analysis

“…NDT fusion is a challenging subject given the dissimilar data acquisition environment of uni-modal inspections; there is some work on combining NDT techniques by fusion rules such as generic maximum, minimum or average merging by pixel-wise, principle component analysis (PCA) and wavelets, as well as AI-based approaches [10,[18][19][20][21]. In this study, we use the maximum combination rule, given in equations ( 1), as a simple yet effective synergistic approach to retrieve defect edges.…”