Name
Abstract | ||
|---|---|---|
Many state-of-the-art fusion methods, combining details in images taken under different exposures into one well-exposed image, can be found in the literature. However, insufficient study has been conducted to explore how perceptual factors can provide viewers better quality of experience on fused images. We propose two perceptual quality measures: perceived local contrast and color saturation, which are embedded in our novel hierarchical multivariate Gaussian conditional random field model, to illustrate improved performance for multi-exposure fusion. We show that our method generates images with better quality than existing methods for a variety of scenes. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/TIP.2012.2236346 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
perceived local contrast,human perception,multi-exposure fusion,gaussian processes,image fusion,random processes,hierarchical multivariate gaussian conditional random field model,color saturation,qoe,perceptual quality factor,conditional random field,quality of experience,multiexposure image fusion method,visual perception,crf,map estimation,image colour analysis,lattices,transducers,estimation,computational modeling,visualization | Image fusion,Computer science,Multivariate normal distribution,Quality of experience,Gaussian process,Artificial intelligence,Visual perception,Conditional random field,Computer vision,Pattern recognition,Stochastic process,Perception,Machine learning | Journal |
Volume | Issue | ISSN |
22 | 6 | 1941-0042 |
Citations | PageRank | References |
17 | 0.62 | 16 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rui Shen | 1 | 128 | 6.03 |
| Irene Cheng | 2 | 283 | 35.18 |
| Anup Basu | 3 | 749 | 97.26 |