# An Overview on the Latest Nature-Inspired and Metaheuristics-Based Image Registration Algorithms
## 1 Introduction  
It has been proven that Image registration (IR) [1] plays a vital role within the computer vision (CV) field. Specifically, IR is one of the most important CV tasks within image processing. It is concerned with the process of accurately aligning two or more images (taken at different times, from different sensors or viewpoints) into a common coordinate system. Then, IR is aimed at finding either a geometric transformation or a correspondence (matching of feature image points) which provides the best possible overlapping of the considered images. It has been applied in a huge amount of real-world applications, medical imaging and remote sensing bwing two of those most addressed in the State-of-The-Art (SoTA). Over the years, different algorithms facing the IR problem have been proposed resulting in a large body of research [2; 3; 4]. To sum up, the IR problem can be formulated from an optimization view-point, in which the IR procedure involves finding the optimal transformation achieving the best fit between the considered images. Such estimation/optimization of the IR transformation is typically addressed by means of an iterative process properly searching in the space of promising IR solutions. In the early contributions to IR, the optimization process carried out by these methods was negatively influenced by the image noise, the image discretization, and orders of magnitude in the scale of the parameters of the IR transformation, among other factors. In particular, it was the case of the canonical Iterative Closest Point (ICP) algorithm [5], in which the process was highly prone to get trapped in local optima IR solutions [6]. Moreover, the latter pitfall is due to that kind of IR method assuming that the alignment procedure starts from accurate pre-alignments of images usually provided by experts. Soft computing (SC) [7] is a term applied to a field within computer science which is characterized by the use of inexact solutions to computationally hard tasks such as the NP-complete problems, in which a global optima solution cannot be derived in (desirable) polynomial time. In particular, SC differs from conventional (hard) computing in that it is tolerant regarding imprecision, uncertainty, partial truth, and approximation. Then, the guiding principle of SC is exploiting the tolerance for such an imprecision, uncertainty, partial truth, and approximation in order to achieve tractability, robustness and low solution cost. The main SC paradigms include fuzzy systems, nature-inspired computation, and artificial neural computing, among others. In the last two decades, nature-inspired computation, e.g., evolutionary computation (EC) [8; 9], has demonstrated its ability to deal with complex real-world problems within the field of CV. Additionally, metaheuristics are other general purpose optimization algorithms successfully applied in this field. As an example, several special issues and books on the topic have been published in international forums in the last few years [10; 11; 12]. Specifically, both the nature-inspired and the metaheuristic (NI&M) approaches have been extensively applied to tackle IR problems without requiring a good initial estimation of the alignment of the images. That advantage is mainly motivated by the global optimization nature of these optimization strategies, which allows them to perform a robust search in complex and ill-defined search spaces. Since the first attempts facing the IR problem using NI&M, this topic has become a very active niche of research and a significant number of proposals have been contributed. Nevertheless, an updated revision of the SoTA is needed due to new and novel enhancements being proposed in the last decade. In this work, we focused on introducing an extensive survey of those most relevant NI&M-based IR methods in the SoTA. Subsequently, it allowed us to provide a more comprehensive analysis of the inherent nature of these new IR methods, in which several quality parameters have been considered for their proper assesment.