Recovering high dynamic range radiance maps from photographs
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
ACM SIGGRAPH 2003 Papers
Digital Image Processing Using MATLAB
Digital Image Processing Using MATLAB
Guiding Model Search Using Segmentation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Seamless Image Stitching of Scenes with Large Motions and Exposure Differences
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Image alignment and stitching: a tutorial
Foundations and Trends® in Computer Graphics and Vision
Background estimation from non-time sequence images
GI '08 Proceedings of graphics interface 2008
Automatic High-Dynamic Range Image Generation for Dynamic Scenes
IEEE Computer Graphics and Applications
TurboPixels: Fast Superpixels Using Geometric Flows
IEEE Transactions on Pattern Analysis and Machine Intelligence
Background estimation using graph cuts and inpainting
Proceedings of Graphics Interface 2010
Ghost-free high dynamic range imaging
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
Fast high dynamic range image deghosting for arbitrary scene motion
Proceedings of Graphics Interface 2012
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High Dynamic Range (HDR) images of real world scenes often suffer from ghosting artifacts caused by motion in the scene. Existing solutions to this problem typically either only address specific types of ghosting, or are very computationally expensive. We address ghosting by performing change detection on exposure-normalized images, then reducing the contribution of moving objects to the final composite on a frame-by-frame basis. Change detection is computationally advantageous and it can be applied to images exhibiting varied ghosting artifacts. We demonstrate our method both for Low Dynamic Range (LDR) and HDR images. Additional constraints based on a priori knowledge of the changing exposures apply to HDR images. We increase the stability of our approach by using recent superpixel segmentation techniques to enhance the change detection. Our solution includes a novel approach for areas that see motion throughout the capture, e.g., foliage blowing in the wind. We demonstrate the success of our approach on challenging ghosting scenarios, and that our results are comparable to existing state-of- the-art methods, while providing computational savings over these methods.