Recovering high dynamic range radiance maps from photographs
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
ACM SIGGRAPH 2003 Papers
ACM SIGGRAPH 2003 Papers
Determining the Camera Response from Images: What Is Knowable?
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Noise reduction in high dynamic range imaging
Journal of Visual Communication and Image Representation
Automatic High-Dynamic Range Image Generation for Dynamic Scenes
IEEE Computer Graphics and Applications
Image enhancement method VIA blur and noisy image fusion
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Robust FFT-Based Scale-Invariant Image Registration with Image Gradients
IEEE Transactions on Pattern Analysis and Machine Intelligence
Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIFT Flow: Dense Correspondence across Scenes and Its Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-rigid dense correspondence with applications for image enhancement
ACM SIGGRAPH 2011 papers
Comparametric equations with practical applications in quantigraphic image processing
IEEE Transactions on Image Processing
A New In-Camera Imaging Model for Color Computer Vision and Its Application
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic noise modeling for ghost-free HDR reconstruction
ACM Transactions on Graphics (TOG)
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Many computational photography applications require the user to take multiple pictures of the same scene with different camera settings. While this allows to capture more information about the scene than what is possible with a single image, the approach is limited by the requirement that the images be perfectly registered. In a typical scenario the camera is hand-held and is therefore prone to moving during the capture of an image burst, while the scene is likely to contain moving objects. Combining such images without careful registration introduces annoying artifacts in the final image. This paper presents a method to register exposure stacks in the presence of both camera motion and scene changes. Our approach warps and modifies the content of the images in the stack to match that of a reference image. Even in the presence of large, highly non-rigid displacements we show that the images are correctly registered to the reference.