Novel Intensity Mapping Functions: Weighted Histogram Averaging
This work addresses image processing issues for applications like photography or computer vision, but it appears incremental as it builds on existing intensity mapping functions.
The paper tackles the challenge of aligning brightness distributions across differently exposed images without color distortion or detail loss by proposing a novel intensity mapping algorithm using weighted histogram averaging (WHA). The results show that the WHA algorithm significantly outperforms state-of-the-art intensity mapping methods in experiments.
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.