Theory and Practice of Projective Rectification
International Journal of Computer Vision
IEEE Computer Graphics and Applications
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Practical Methods for Geometric and Photometric Correction of Tiled Projector
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Robust Radiometric Calibration and Vignetting Correction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Projective rectification from the fundamental matrix
Image and Vision Computing
Iterative colour correction of multicamera systems using corresponding feature points
Journal of Visual Communication and Image Representation
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Geometric and shading correction for images of printed materials using boundary
IEEE Transactions on Image Processing
Multiview Video Coding Using View Interpolation and Color Correction
IEEE Transactions on Circuits and Systems for Video Technology
Histogram-Based Prefiltering for Luminance and Chrominance Compensation of Multiview Video
IEEE Transactions on Circuits and Systems for Video Technology
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In general, excessive colorimetric and geometric errors in multi-view images induce visual fatigue to users. Various works have been proposed to reduce these errors, but conventional works have only been available for stereoscopic images while requiring cumbersome additional tasks, and often showing unstable results. In this paper, we propose an effective multi-view image refinement algorithm. The proposed algorithm analyzes such errors in multi-view images from sparse correspondences and compensates them automatically. While the conventional works transform every view to compensate geometric errors, the proposed method transforms only the source views with consideration of a reference view. Therefore this approach can be extended regardless of the number of views. In addition, we also employ uniform view intervals to provide consistent depth perception among views. We correct color inconsistency among views from the correspondences by considering importance and channel properties. Various experimental results show that the proposed algorithm outperforms conventional approaches and generates more visually comfortable multi-view images.