Automatic Exposure Compensation for Multi-Exposure Image Fusion
This work addresses a specific bottleneck in multi-exposure image fusion for computer vision applications, offering an incremental improvement over existing methods.
The paper tackles the problem of determining the degree of luminance adjustment in multi-exposure image fusion by proposing an automatic exposure compensation method based on luminance distribution, resulting in improved image quality with better well-exposedness and the highest scores in statistical naturalness and discrete entropy using new simple weights.
This paper proposes a novel luminance adjustment method based on automatic exposure compensation for multi-exposure image fusion. Multi-exposure image fusion is a method to produce images without saturation regions, by using photos with different exposures. In conventional works, it has been pointed out that the quality of those multi-exposure images can be improved by adjusting the luminance of them. However, how to determine the degree of adjustment has never been discussed. This paper therefore proposes a way to automatically determines the degree on the basis of the luminance distribution of input multi-exposure images. Moreover, new weights, called "simple weights", for image fusion are also considered for the proposed luminance adjustment method. Experimental results show that the multi-exposure images adjusted by the proposed method have better quality than the input multi-exposure ones in terms of the well-exposedness. It is also confirmed that the proposed simple weights provide the highest score of statistical naturalness and discrete entropy in all fusion methods.