Novel Intensity Mapping Functions: Weighted Histogram Averaging

TL;DR

This paper introduces a new intensity mapping algorithm called weighted histogram averaging (WHA) that improves alignment of brightness distributions across images with different exposures, reducing color distortion and detail loss.

Contribution

The paper presents a novel intensity mapping algorithm using weighted histogram averaging based on histogram bin correspondence and the non-decreasing property of IMFs.

Findings

WHA significantly outperforms existing intensity mapping methods.

Extensive experiments validate the effectiveness of the proposed approach.

The method reduces color distortion and detail loss in exposure correction.

Abstract

It is challenging to align the brightness distribution of the images with different exposures due to possible color distortion and loss of details in the brightest and darkest regions of input images. In this paper, a novel intensity mapping algorithm is first proposed by introducing a new concept of weighted histogram averaging (WHA). The proposed WHA algorithm leverages the correspondence between the histogram bins of two images which are built up by using the non-decreasing property of the intensity mapping functions (IMFs). Extensive experiments indicate that the proposed WHA algorithm significantly surpasses the related state-of-the-art intensity mapping methods.