Image deblurring using inertial measurement sensors
By Neel Joshi Sing, Bing Kang, C. Lawrence, Zitnick Richard Szeliski, Gyros ArduinoBoard. ACM Transactions on Graphics, 2010
330 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Media Technology
View Paper: https://www.semanticscholar.org/paper/7b3974f9809faa174bb145903b423df5aa0fa0c0
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Ming-Hsuan Yang
- Luc Van Gool
- Dacheng Tao
- Andrew Zisserman
- Trevor Darrell
- Xiaogang Wang
- Jitendra Malik
- Andrea Vedaldi
In Media Technology
- Michael Hirsch
- Ming-Hsuan Yang
- Jiaya Jia
- Jean Ponce
- Bernhard Schölkopf
- Li Xu
- Shicheng Zheng
- Josef Sivic
Citations in the corpus
- Fast removal of non-uniform camera shake (2011, background): “[8] exploit motion sensor information to recover the true trajectory of the camera during the shake.”
- Unnatural L0 Sparse Representation for Natural Image Deblurring (2013, background): “[12] advocated a hardware solution that physically captures camera rotation.”
- Non-uniform Deblurring for Shaken Images (2010, methodology): “A hand-held image with camera shake (a), from (Joshi et al. 2010), with the deblurred results from the original work (b) and using the MAP-l1 method with our blur model (c).”
- Blind deconvolution in scientific imaging & computational photography (2011, methodology): “In (Joshi et al., 2010), it has been demonstrated that such sensors are capable of recording the camera trajectory during exposure precisely enough to yield high-quality deblurring results. However, their presented system relies on a complicated and subtle calibration step that renders their approac”
- Richardson-Lucy Deblurring for Scenes under a Projective Motion Path (2011, methodology): “Another promising hardware coupling is the use of accelerometers and inertial sensors for estimating the cameras motion as demonstrated in [12] and [14].”
- Single Image Deblurring Using Motion Density Functions (2010, background): “Joshi et al. [14] do this in a limited sense, where they run their method for non-overlapping windows in an image and use this to remove spatially varying defocus blur and chromatic aberration; however, they do not address camera motion blur, nor do they try to recover a global model of the blur.”