Dictionary Learning Based Pan-Sharpening

TR Image

Pan-sharpening is an image fusion process in which high resolution (HR) panchromatic (Pan) imagery is used to sharpen the corresponding low resolution (LR) multi-spectral (MS) imagery. Pan-sharpened MS images generally have high spatial resolutions, but exhibit color distortions. In this paper, we propose a dictionary learning based pan-sharpening process to reduce the color distortion caused by the interpolation of the MS imagery. Instead of interpolating the LR MS image before fusion, we generate an improved MS image which is sparse with respect to a dictionary learned from the image data. Our experiments on degraded QuickBird and IKONOS images demonstrate that the distortion in the MS images produced using our approach is significantly reduced.


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    •  NEWS    ICASSP 2012: 8 publications by Petros T. Boufounos, Dehong Liu, John R. Hershey, Jonathan Le Roux and Zafer Sahinoglu
      Date: March 25, 2012
      Where: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
      MERL Contacts: Dehong Liu; Jonathan Le Roux; Petros T. Boufounos
      • The papers "Dictionary Learning Based Pan-Sharpening" by Liu, D. and Boufounos, P.T., "Multiple Dictionary Learning for Blocking Artifacts Reduction" by Wang, Y. and Porikli, F., "A Compressive Phase-Locked Loop" by Schnelle, S.R., Slavinsky, J.P., Boufounos, P.T., Davenport, M.A. and Baraniuk, R.G., "Indirect Model-based Speech Enhancement" by Le Roux, J. and Hershey, J.R., "A Clustering Approach to Optimize Online Dictionary Learning" by Rao, N. and Porikli, F., "Parametric Multichannel Adaptive Signal Detection: Exploiting Persymmetric Structure" by Wang, P., Sahinoglu, Z., Pun, M.-O. and Li, H., "Additive Noise Removal by Sparse Reconstruction on Image Affinity Nets" by Sundaresan, R. and Porikli, F. and "Depth Sensing Using Active Coherent Illumination" by Boufounos, P.T. were presented at the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP).