Multi-Exposure Image Fusion Based on Exposure Compensation
This work addresses a specific challenge in image processing for photographers and researchers, but it is incremental as it builds on existing fusion methods.
The paper tackles the problem of determining appropriate exposure values in multi-exposure image fusion by proposing a method based on exposure compensation and local contrast enhancement, resulting in improved image quality as evaluated through metrics like tone mapped image quality index, statistical naturalness, and discrete entropy.
This paper proposes a novel multi-exposure image fusion method based on exposure compensation. Multi-exposure image fusion is a method to produce images without color saturation regions, by using photos with different exposures. However, in conventional works, it is unclear how to determine appropriate exposure values, and moreover, it is difficult to set appropriate exposure values at the time of photographing due to time constraints. In the proposed method, the luminance of the input multi-exposure images is adjusted on the basis of the relationship between exposure values and pixel values, where the relationship is obtained by assuming that a digital camera has a linear response function. The use of a local contrast enhancement method is also considered to improve input multi-exposure images. The compensated images are finally combined by one of existing multi-exposure image fusion methods. In some experiments, the effectiveness of the proposed method are evaluated in terms of the tone mapped image quality index, statistical naturalness, and discrete entropy, by comparing the proposed one with conventional ones.