Super-Resolution Reconstruction for Multi-Angle Remote Sensing Images Considering Resolution Differences

Multi-angle remote sensing images are acquired over the same imaging scene from different angles, and share similar but not identical information. It is therefore possible to enhance the spatial resolution of the multi-angle remote sensing images by the super-resolution reconstruction technique. However, different sensor shooting angles lead to different resolutions for each angle image, which affects the effectiveness of the super-resolution reconstruction of the multi-angle images. In view of this, we propose utilizing adaptive weighted super-resolution reconstruction to alleviate the limitations of the different resolutions. This paper employs two adaptive weighting themes. The first approach uses the angle between the imaging angle of the current image and that of the nadir image. The second is closely related to the residual error of each low-resolution angle image. The experimental results confirm the feasibility of the proposed method and demonstrate the effectiveness of the proposed adaptive weighted super-resolution approach.

[1]  Moon Gi Kang,et al.  Super-resolution image reconstruction: a technical overview , 2003, IEEE Signal Process. Mag..

[2]  Michael Elad,et al.  Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images , 1997, IEEE Trans. Image Process..

[3]  Michal Irani,et al.  Improving resolution by image registration , 1991, CVGIP Graph. Model. Image Process..

[4]  Jianglin Ma,et al.  Superresolution Enhancement of Hyperspectral CHRIS/Proba Images With a Thin-Plate Spline Nonrigid Transform Model , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Jianglin Ma,et al.  An Operational Superresolution Approach for Multi-Temporal and Multi-Angle Remotely Sensed Imagery , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[6]  Lisimachos P. Kondi,et al.  An image super-resolution algorithm for different error levels per frame , 2006, IEEE Transactions on Image Processing.

[7]  Xiang Zhu,et al.  Automatic Parameter Selection for Denoising Algorithms Using a No-Reference Measure of Image Content , 2010, IEEE Transactions on Image Processing.

[8]  Aggelos K. Katsaggelos,et al.  Digital image restoration , 2012, IEEE Signal Process. Mag..

[9]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[10]  C. Vogel.  Computational Methods for Inverse Problems , 1987 .

[11]  Michael Elad,et al.  Advances and challenges in super‐resolution , 2004, Int. J. Imaging Syst. Technol..

[12]  Daniel Gross,et al.  Improved resolution from subpixel shifted pictures , 1992, CVGIP Graph. Model. Image Process..

[13]  Liangpei Zhang,et al.  A super-resolution reconstruction algorithm for surveillance images , 2010, Signal Process..

[14]  José M. Bioucas-Dias,et al.  Adaptive total variation image deconvolution: A majorization-minimization approach , 2006, 2006 14th European Signal Processing Conference.

[15]  Li Chen,et al.  A soft MAP framework for blind super-resolution image reconstruction , 2009, Image Vis. Comput..

[16]  Aggelos K. Katsaggelos,et al.  Reconstruction of a high-resolution image by simultaneous registration, restoration, and interpolation of low-resolution images , 1995, Proceedings., International Conference on Image Processing.

[17]  Michael K. Ng,et al.  Super-Resolution Reconstruction Algorithm To MODIS Remote Sensing Images , 2009, Comput. J..

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

[19]  Michael Elad,et al.  Fast and robust multiframe super resolution , 2004, IEEE Transactions on Image Processing.

[20]  Sabine Süsstrunk,et al.  A Frequency Domain Approach to Registration of Aliased Images with Application to Super-resolution , 2006, EURASIP J. Adv. Signal Process..

[21]  James Theiler,et al.  Resolution enhancement of multilook imagery for the multispectral thermal imager , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[22]  Qian Du,et al.  2011 GRSS Data Fusion Contest: Exploiting WorldView-2 multi-angular acquisitions , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.

[23]  H Stark,et al.  High-resolution image recovery from image-plane arrays, using convex projections. , 1989, Journal of the Optical Society of America. A, Optics and image science.

[24]  Dana H. Brooks,et al.  Electrical imaging of the heart , 1997, IEEE Signal Process. Mag..

[25]  C. Latry,et al.  Super resolution: quincunx sampling and fusion processing , 2003, IGARSS.

[26]  Feng Li,et al.  Super Resolution for Remote Sensing Images Based on a Universal Hidden Markov Tree Model , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[27]  Nikolas P. Galatsanos,et al.  Reconstruction of a high resolution image from registration and restoration of low resolution images , 1994, Proceedings of 1st International Conference on Image Processing.

[28]  Edmund Y. Lam,et al.  A Total Variation Regularization Based Super-Resolution Reconstruction Algorithm for Digital Video , 2007, EURASIP J. Adv. Signal Process..

[29]  Roger Y. Tsai,et al.  Multiframe image restoration and registration , 1984 .

[30]  Yücel Altunbasak,et al.  Super-resolution reconstruction of hyperspectral images , 2005 .

[31]  Robert L. Stevenson,et al.  Extraction of high-resolution frames from video sequences , 1996, IEEE Trans. Image Process..

[32]  Lina J. Karam,et al.  A No-Reference Image Blur Metric Based on the Cumulative Probability of Blur Detection (CPBD) , 2011, IEEE Transactions on Image Processing.

[33]  Liangpei Zhang,et al.  A super-resolution reconstruction algorithm for hyperspectral images , 2012, Signal Process..