# A Probabilistic Bitwise Genetic Algorithm for B-Spline based Image Deformation Estimation

## TL;DR

This paper introduces a probabilistic bitwise genetic algorithm with a novel operation and annealing selection to improve image deformation estimation using 2D cubic B-spline models, effectively handling complex distortions.

## Contribution

It presents a new genetic algorithm with probabilistic bitwise operations and annealing selection for better diversity and convergence in B-spline based image deformation estimation.

## Key findings

- Effective in synthetic data tests
- Preserves genetic diversity during evolution
- Achieves better solution coverage

## Abstract

We propose a novel genetic algorithm to solve the image deformation estimation problem by preserving the genetic diversity. As a classical problem, there is always a trade-off between the complexity of deformation models and the difficulty of parameters search in image deformation. 2D cubic B-spline surface is a highly free-form deformation model and is able to handle complex deformations such as fluid image distortions. However, it is challenging to estimate an apposite global solution. To tackle this problem, we develop a genetic operation named probabilistic bitwise operation (PBO) to replace the crossover and mutation operations, which can preserve the diversity during generation iteration and achieve better coverage ratio of the solution space. Furthermore, a selection strategy named annealing selection is proposed to control the convergence. Qualitative and quantitative results on synthetic data show the effectiveness of our method.