Robust Alignment of Multi-Exposed Images with Saturated Regions

TL;DR

This paper introduces a new algorithm for aligning multi-exposed images with saturated regions by normalization, coding, and optimization, significantly improving accuracy and robustness over existing methods.

Contribution

It presents a novel alignment method that effectively handles saturation and illumination variations through intensity normalization, LBP coding, and a differentiable Hamming distance optimization.

Findings

Outperforms existing methods in alignment accuracy

Demonstrates robustness to exposure variations

Effective handling of saturated regions in images

Abstract

It is challenging to align multi-exposed images due to large illumination variations, especially in presence of saturated regions. In this paper, a novel image alignment algorithm is proposed to cope with the multi-exposed images with saturated regions. Specifically, the multi-exposed images are first normalized by using intensity mapping functions (IMFs) in consideration of saturated pixels. Then, the normalized images are coded by using the local binary pattern (LBP). Finally, the coded images are aligned by formulating an optimization problem by using a differentiable Hamming distance. Experimental results show that the proposed algorithm outperforms state-of-the-art alignment methods for multi-exposed images in terms of alignment accuracy and robustness to exposure values.