Multi-projector Resolution Enhancement Through Biased Interpolation

Projecting the same content with multiple overlapping projectors provides several advantages compared to using a single projector: increased brightness to overcome ambient light or projection surface anomalies, redundancy in case of projector failure, an increase in the area being projected on, and the possibility for increased content resolution. Multi-projector resolution enhancement is the process of using multiple projectors to achieve a resolution greater than any individual projector in the configuration. Current resolution enhancement techniques perform filtering on the sub-images produced by each projector using spatial or frequency based filters. The kernel based filtering adds significant overhead relative to the interpolation calculations. In addition the learned filters are extremely sensitive to calibration. This work develops a method for performing multi-projector resolution enhancement by integrating the filtering into the interpolation process. A system is developed to jointly condition multiple low resolution sub-images on each other to approximate high resolution original content.

[1]  Mark Lamm,et al.  35.3: Resolution Enhancement Based on Shifted Superposition , 2015 .

[2]  Fred L. Bookstein,et al.  Principal Warps: Thin-Plate Splines and the Decomposition of Deformations , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Daniel G. Aliaga,et al.  Fast high-resolution appearance editing using superimposed projections , 2012, TOGS.

[4]  Robert Ulichney,et al.  47.4: Invited Paper: Wobulation: Doubling the Addressed Resolution of Projection Displays , 2005 .

[5]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[6]  Meenakshisundaram Gopi,et al.  Image enhancement in projectors via optical pixel shift and overlay , 2013, IEEE International Conference on Computational Photography (ICCP).

[7]  Niranjan Damera-Venkata,et al.  On the Resolution Limits of Superimposed Projection , 2007, 2007 IEEE International Conference on Image Processing.

[8]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[9]  Amir Said Analysis of Subframe Generation for Superimposed Images , 2006, 2006 International Conference on Image Processing.

[10]  Richard Szeliski,et al.  Computer Vision - Algorithms and Applications , 2011, Texts in Computer Science.

[11]  Christopher Jaynes,et al.  Super-Resolution Composition in Multi-Projector Displays , 2003 .

[12]  Jon Yngve Hardeberg,et al.  Resolution enhancement through superimposition of projected images – How to evaluate the quality? , 2017 .

[13]  Ning Qian,et al.  On the momentum term in gradient descent learning algorithms , 1999, Neural Networks.