A spatially adaptive retinex variational model for the uneven intensity correction of remote sensing images

In this paper, a spatially adaptive retinex variational model for the uneven intensity correction of remote sensing images is proposed. In the model, the spatial information is used to constrain the TV regularization strength of the reflectance. In the edge pixels, a weak regularization strength is enforced to preserve detail, and in the homogeneous areas, a strong regularization strength is enforced to eliminate the uneven intensity. The relationship and the fidelity term between the illumination and reflectance are also considered. Moreover, the split Bregman optimization algorithm is employed to solve the proposed model. The experimental results with both simulated and real-life data demonstrate that the proposed method is effective, based on both the visual effect and quantitative assessment. A spatially adaptive retinex variational model for the uneven intensity correction of remote sensing images was proposed.The spatial information was used to constrain the TV regularization strength of the reflectance.The relationship and the fidelity term between the illumination and reflectance were considered.The split Bregman optimization algorithm was employed to solve the proposed model.

[1]  Heng Lian,et al.  Total variation, adaptive total variation and nonconvex smoothly clipped absolute deviation penalty for denoising blocky images , 2009, Pattern Recognit..

[2]  Xuelong Li,et al.  A multi-frame image super-resolution method , 2010, Signal Process..

[3]  H. Yeganeh,et al.  A novel approach for contrast enhancement based on Histogram Equalization , 2008, 2008 International Conference on Computer and Communication Engineering.

[4]  Heung-Kook Choi,et al.  Image contrast enhancement using bi-histogram equalization with neighborhood metrics , 2010, IEEE Transactions on Consumer Electronics.

[5]  Tony F. Chan,et al.  Image processing and analysis - variational, PDE, wavelet, and stochastic methods , 2005 .

[6]  Alessandro Rizzi,et al.  Mathematical definition and analysis of the retinex algorithm. , 2005, Journal of the Optical Society of America. A, Optics, image science, and vision.

[7]  Dacheng Tao,et al.  Simple Exponential Family PCA , 2010, IEEE Transactions on Neural Networks and Learning Systems.

[8]  Weisi Lin,et al.  Image Quality Assessment Based on Gradient Similarity , 2012, IEEE Transactions on Image Processing.

[9]  Agya Mishra,et al.  Color Image Enhancement Techniques: A Critical Review , 2012 .

[10]  Glenn Healey,et al.  Hyperspectral Texture Synthesis Using Histogram and Power Spectral Density Matching , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Michael Elad,et al.  A Variational Framework for Retinex , 2002, IS&T/SPIE Electronic Imaging.

[12]  Weifeng Liu,et al.  Multiview Hessian Regularization for Image Annotation , 2013, IEEE Transactions on Image Processing.

[13]  Carlo Gatta,et al.  A Spatially Variant White-Patch and Gray-World Method for Color Image Enhancement Driven by Local Contrast , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Liangpei Zhang,et al.  Single image haze removal considering sensor blur and noise , 2013, EURASIP J. Adv. Signal Process..

[15]  A. Sreenivasa Murthy,et al.  A comparison between different colour image contrast enhancement algorithms , 2011, 2011 International Conference on Emerging Trends in Electrical and Computer Technology.

[16]  Andrew Blake,et al.  Boundary conditions for lightness computation in Mondrian World , 1985, Comput. Vis. Graph. Image Process..

[17]  Xuelong Li,et al.  Single Image Super-Resolution With Non-Local Means and Steering Kernel Regression , 2012, IEEE Transactions on Image Processing.

[18]  Tom Goldstein,et al.  The Split Bregman Method for L1-Regularized Problems , 2009, SIAM J. Imaging Sci..

[19]  Pheng-Ann Heng,et al.  Adaptive total variation denoising based on difference curvature , 2010, Image Vis. Comput..

[20]  Robert S. Fraser,et al.  The effect of the atmosphere on the classification of satellite observations to identify surface features , 1977 .

[21]  Jean-Michel Morel,et al.  A PDE Formalization of Retinex Theory , 2010, IEEE Transactions on Image Processing.

[22]  Heung-Kook Choi,et al.  Brightness preserving weight clustering histogram equalization , 2008, IEEE Transactions on Consumer Electronics.

[23]  Alessandro Rizzi,et al.  A computational approach to color adaptation effects , 2000, Image Vis. Comput..

[24]  Joongkyu Kim,et al.  Retinex method based on adaptive smoothing for illumination invariant face recognition , 2008, Signal Process..

[25]  Michael K. Ng,et al.  A Total Variation Model for Retinex , 2011, SIAM J. Imaging Sci..

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

[27]  Tony F. Chan,et al.  Spatially adaptive local-feature-driven total variation minimizing image restoration , 1997, Optics & Photonics.

[28]  Liangpei Zhang,et al.  Adaptive Multiple-Frame Image Super-Resolution Based on U-Curve , 2010, IEEE Transactions on Image Processing.

[29]  Liangpei Zhang,et al.  A Perceptually Inspired Variational Method for the Uneven Intensity Correction of Remote Sensing Images , 2012, IEEE Transactions on Geoscience and Remote Sensing.

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

[31]  Liangpei Zhang,et al.  Multiframe Super-Resolution Employing a Spatially Weighted Total Variation Model , 2012, IEEE Transactions on Circuits and Systems for Video Technology.

[32]  Xuelong Li,et al.  Color to Gray: Visual Cue Preservation , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[33]  Xuelong Li,et al.  General Tensor Discriminant Analysis and Gabor Features for Gait Recognition , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[34]  R. Uthayakumar,et al.  Detecting Patterns in Irregular Time Series with Fractal Dimension , 2007, International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007).

[35]  Tony F. Chan,et al.  Image processing and analysis , 2005 .

[36]  Bastian Goldlücke,et al.  Total Variation , 2014, Computer Vision, A Reference Guide.

[37]  Demetri Terzopoulos,et al.  Image Analysis Using Multigrid Relaxation Methods , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[38]  Farhan A. Baqai,et al.  Analysis and extensions of the Frankle-McCann Retinex algorithm , 2004, J. Electronic Imaging.

[39]  Dacheng Tao,et al.  Double Shrinking Sparse Dimension Reduction , 2013, IEEE Transactions on Image Processing.

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

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

[42]  S.R. Pudale,et al.  Comparative Study of Relative Radiometric Normalization Techniques for Resourcesat1 LISS III Sensor Images , 2007, International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007).

[43]  H. Ibrahim,et al.  A review: Image compensation techniques , 2010, 2010 2nd International Conference on Computer Engineering and Technology.

[44]  E. Land,et al.  Lightness and retinex theory. , 1971, Journal of the Optical Society of America.

[45]  Wilhelm Burger,et al.  Digital Image Processing - An Algorithmic Introduction using Java , 2008, Texts in Computer Science.

[46]  C. Woodcock,et al.  Classification and Change Detection Using Landsat TM Data: When and How to Correct Atmospheric Effects? , 2001 .

[47]  Hermann Kaufmann,et al.  Lake water quality monitoring using hyperspectral airborne data—a semiempirical multisensor and multitemporal approach for the Mecklenburg Lake District, Germany , 2002 .

[48]  Jiebo Luo,et al.  Probabilistic Exposure Fusion , 2012, IEEE Transactions on Image Processing.

[49]  Edoardo Provenzi,et al.  A Perceptually Inspired Variational Framework for Color Enhancement , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[50]  Berthold K. P. Horn,et al.  Determining lightness from an image , 1974, Comput. Graph. Image Process..

[51]  Gabriel Thomas,et al.  Histogram Specification: A Fast and Flexible Method to Process Digital Images , 2011, IEEE Transactions on Instrumentation and Measurement.

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