Recovering high dynamic range radiance maps from photographs
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
Modeling the Space of Camera Response Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Determining the Radiometric Response Function from a Single Grayscale Image
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Removing camera shake from a single photograph
ACM SIGGRAPH 2006 Papers
Image deblurring with blurred/noisy image pairs
ACM SIGGRAPH 2007 papers
Robust Radiometric Calibration and Vignetting Correction
IEEE Transactions on Pattern Analysis and Machine Intelligence
High-quality motion deblurring from a single image
ACM SIGGRAPH 2008 papers
ACM SIGGRAPH Asia 2009 papers
A New Alternating Minimization Algorithm for Total Variation Image Reconstruction
SIAM Journal on Imaging Sciences
Correction of Spatially Varying Image and Video Motion Blur Using a Hybrid Camera
IEEE Transactions on Pattern Analysis and Machine Intelligence
Radiometric calibration from a single image
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Blind deconvolution using a normalized sparsity measure
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
RGB calibration for color image analysis in machine vision
IEEE Transactions on Image Processing
Blind inverse gamma correction
IEEE Transactions on Image Processing
Hi-index | 0.00 |
Motion deblurring is a long standing problem in computer vision and image processing. In most previous approaches, the blurred image is modeled as the convolution of a latent intensity image with a blur kernel. However, for images captured by a real camera, the blur convolution should be applied to scene irradiance instead of image intensity and the blurred results need to be mapped back to image intensity via the camera's response function (CRF). In this paper, we present a comprehensive study to analyze the effects of CRFs on motion deblurring. We prove that the intensity-based model closely approximates the irradiance model at low frequency regions. However, at high frequency regions such as edges, the intensity-based approximation introduces large errors and directly applying deconvolution on the intensity image will produce strong ringing artifacts even if the blur kernel is invertible. Based on the approximation error analysis, we further develop a dual-image based solution that captures a pair of sharp/blurred images for both CRF estimation and motion deblurring. Experiments on synthetic and real images validate our theories and demonstrate the robustness and accuracy of our approach.