Two-Exposure Image Fusion Based on Optimized Adaptive Gamma Correction

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 1; p. 24
• Main Authors: Peng, Yan-Tsung, Liao, He-Hao, Chen, Ching-Fu
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 22.12.2021; MDPI
• Subjects: Algorithms; Cameras; Computer vision; Digital imaging; Exposure; gamma correction; high dynamic imaging; Image contrast; Image enhancement; Image processing; Image quality; Sensors; Standard deviation; two-exposure image fusion; high dynamic imaging; two-exposure image fusion; gamma correction
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22010024

In contrast to conventional digital images, high-dynamic-range (HDR) images have a broader range of intensity between the darkest and brightest regions to capture more details in a scene. Such images are produced by fusing images with different exposure values (EVs) for the same scene. Most existing multi-scale exposure fusion (MEF) algorithms assume that the input images are multi-exposed with small EV intervals. However, thanks to emerging spatially multiplexed exposure technology that can capture an image pair of short and long exposure simultaneously, it is essential to deal with two-exposure image fusion. To bring out more well-exposed contents, we generate a more helpful intermediate virtual image for fusion using the proposed Optimized Adaptive Gamma Correction (OAGC) to have better contrast, saturation, and well-exposedness. Fusing the input images with the enhanced virtual image works well even though both inputs are underexposed or overexposed, which other state-of-the-art fusion methods could not handle. The experimental results show that our method performs favorably against other state-of-the-art image fusion methods in generating high-quality fusion results.