Hyperspectral urban remote sensing image smoothing and enhancement using forward-and-backward diffusion

Anisotropic diffusion has received a lot of attention and has experienced significant developments, with promising results and applications in several specific domains. In this paper, a general flexible class of hyperspectral forward-and-backward (FAB) diffusion process will be proposed, which can achieve the main requirements for edge-preserving regularization with image enhancement. In addition, we use additive operator splitting (AOS) scheme to speedup the numerical evolution of the nonlinear diffusion equation with respect to traditional explicit schemes. The performance of the vector-valued FAB diffusion PDE is studied using one hyperspectral remote sensing image. Experimental results on these images are shown the validity and effectiveness of the proposed method.

[1]  Yehoshua Y. Zeevi,et al.  Forward-and-backward diffusion processes for adaptive image enhancement and denoising , 2002, IEEE Trans. Image Process..

[2]  Aldo Cumani,et al.  Edge detection in multispectral images , 1991, CVGIP Graph. Model. Image Process..

[3]  Pavel Mrázek.  Selection of Optimal Stopping Time for Nonlinear Diffusion Filtering , 2001, Scale-Space.

[4]  Scott T. Acton,et al.  Multigrid anisotropic diffusion , 1998, IEEE Trans. Image Process..

[5]  Jayaram K. Udupa,et al.  Scale-Based Diffusive Image Filtering Preserving Boundary Sharpness and Fine Structures , 2002, IEEE Trans. Medical Imaging.

[6]  Guillermo Sapiro,et al.  Anisotropic diffusion of multivalued images with applications to color filtering , 1996, IEEE Trans. Image Process..

[7]  Yi Wang,et al.  Local Variance-Controlled Forward-and-Backward Diffusion for Image Enhancement and Noise Reduction , 2007, IEEE Transactions on Image Processing.

[8]  P. Lions,et al.  Image selective smoothing and edge detection by nonlinear diffusion. II , 1992 .

[9]  Silvano Di Zenzo,et al.  A note on the gradient of a multi-image , 1986, Comput. Vis. Graph. Image Process..

[10]  Ke Chen,et al.  Adaptive smoothing via contextual and local discontinuities , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Steven W. Zucker,et al.  Local Scale Control for Edge Detection and Blur Estimation , 1996, ECCV.

[12]  Grégoire Mercier,et al.  Nonlinear filtering of hyperspectral images with anisotropic diffusion , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[13]  Scott T. Acton,et al.  Modified mean curvature motion for multispectral anisotropic diffusion , 1998, 1998 IEEE Southwest Symposium on Image Analysis and Interpretation (Cat. No.98EX165).

[14]  S. Acton,et al.  Multi-spectral anisotropic diffusion , 1997 .

[15]  G. Sapiro,et al.  Geometric partial differential equations and image analysis [Book Reviews] , 2001, IEEE Transactions on Medical Imaging.

[16]  Wenyuan Xu,et al.  Behavioral analysis of anisotropic diffusion in image processing , 1996, IEEE Trans. Image Process..

[17]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Max A. Viergever,et al.  Efficient and reliable schemes for nonlinear diffusion filtering , 1998, IEEE Trans. Image Process..

[19]  Yehoshua Y. Zeevi,et al.  Image enhancement segmentation and denoising by time dependent nonlinear diffusion processes , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[20]  Guido Gerig,et al.  Nonlinear anisotropic filtering of MRI data , 1992, IEEE Trans. Medical Imaging.

[21]  Guillermo Sapiro,et al.  Robust anisotropic diffusion , 1998, IEEE Trans. Image Process..

[22]  Julio Martín-Herrero,et al.  Anisotropic Diffusion in the Hypercube , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[23]  N. Sochen,et al.  Image Sharpening by Flows Based on Triple Well Potentials , 2004 .

[24]  Miguel Velez-Reyes,et al.  Comparative Study of Semi-Implicit Schemes for Nonlinear Diffusion in Hyperspectral Imagery , 2007, IEEE Transactions on Image Processing.

[25]  Joachim Weickert,et al.  Theoretical Foundations of Anisotropic Diffusion in Image Processing , 1994, Theoretical Foundations of Computer Vision.