BibTeX:

@article{IJIRSTV1I11120,
     title={SELECTIVE DETAIL ENHANCED FUSION WITH PHOTOCROPPING},
     author={ROOPA TEENA JOHNSON, MANU G THOMAS and AMBIKADEVIAMMA T},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={1},
     number={11},
     pages={401--405},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV1I11120.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

The display of a natural scene which exhibits high dynamic range (HDR) on a conventional low dynamic range (LDR) display is normally a challenging task. To solve this problem multiple differently exposed images are captured and fused together into a detailed image. In this paper, a ghost removal algorithm is performed to convert non-consistent pixels into consistent pixels and the corrected image is fused using a selectively detail enhanced exposure fusion algorithm. Thus a detail enhanced images is produced as a result. To this detail enhanced image a photocropping technique uses an unsupervised fuzzy clustering algorithm which converts the image into atomic regions. A manifold embedding algorithm is used for image-level semantics and image global configurations with graphlets or a small-sized connected subgraph. Bayesian network (BN) makes the photo into the framework derived from the multi-channel post-embedding graphlets of the image data. The cropping parameters are calculated by Gibbs sampling method and finally, the enhanced cropped image will be obtained.

Cropping, Bayesian network, Exposure Fusion, Graphlets, Natural scene