Multiresolution image fusion using edge-preserving filters

Abstract. We propose two approaches of multiresolution image fusion using multistage guided filter and difference of Gaussians (DoGs). In a multiresolution image fusion problem, the given multispectral (MS) and panchromatic (Pan) images have high spectral and high spatial resolutions, respectively. One can obtain the fused image using these two images by injecting the missing high frequency details from the Pan image into the MS image. The quality of the final fused image will then depend on the method used for high frequency details extraction and also on the technique for injecting these details into the MS image. Specifically, we have chosen the guided filter and DoGs for detail extraction since these are more versatile in applications involving feature extraction, denoising, and so on. The detail extraction process in the fusion approach using a guided filter exploits the relationship between the Pan and MS images by utilizing one of them as a guidance image while extracting details from the other. The final fused image is obtained by adding the extracted high frequency details to the corresponding MS image. This way, the spatial distortion of the MS image is reduced by consistently combining the details obtained using both MS and Pan images. In the fusion method using DoGs, the high frequency details are extracted in the first and second levels by subtracting the blurred images of the original Pan. The extracted details at both DoGs are added to the MS image to obtain the final fused image. Advantages and disadvantages of each method are discussed and the comparison of the results is shown between the two. The results are also compared with the traditional and the state-of-the-art methods using the images captured using different satellites such as Quickbird, Ikonos-2, and Worldview-2. The quantitative assessment is evaluated using the conventional measures as well as using a relatively new index, i.e., quality with no reference which does not require a reference image. The results and measures clearly show that there is promising improvement in the quality of the fused image using the proposed approaches.

[1]  Russell C. Hardie,et al.  MAP estimation for hyperspectral image resolution enhancement using an auxiliary sensor , 2004, IEEE Transactions on Image Processing.

[2]  Roger L. King,et al.  An Efficient Pan-Sharpening Method via a Combined Adaptive PCA Approach and Contourlets , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Xavier Otazu,et al.  Multiresolution-based image fusion with additive wavelet decomposition , 1999, IEEE Trans. Geosci. Remote. Sens..

[4]  Kishor P. Upla,et al.  Multi-resolution image fusion using multistage guided filter , 2013, 2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG).

[5]  Andrea Garzelli,et al.  Context-driven fusion of high spatial and spectral resolution images based on oversampled multiresolution analysis , 2002, IEEE Trans. Geosci. Remote. Sens..

[6]  Shutao Li,et al.  Image Fusion With Guided Filtering , 2013, IEEE Transactions on Image Processing.

[7]  Jian Sun,et al.  Guided Image Filtering , 2010, ECCV.

[8]  S. Baronti,et al.  Multispectral and panchromatic data fusion assessment without reference , 2008 .

[9]  Te-Ming Tu,et al.  A fast intensity-hue-saturation fusion technique with spectral adjustment for IKONOS imagery , 2004, IEEE Geoscience and Remote Sensing Letters.

[10]  W. Shi,et al.  Wavelet-based image fusion and quality assessment , 2005 .

[11]  Yaonan Wang,et al.  Using the discrete wavelet frame transform to merge Landsat TM and SPOT panchromatic images , 2002, Inf. Fusion.

[12]  Richard Bamler,et al.  A Sparse Image Fusion Algorithm With Application to Pan-Sharpening , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Xavier Otazu,et al.  Introduction of sensor spectral response into image fusion methods. Application to wavelet-based methods , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Manjunath V. Joshi,et al.  MAP Estimation for Multiresolution Fusion in Remotely Sensed Images Using an IGMRF Prior Model , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Liangpei Zhang,et al.  A Practical Compressed Sensing-Based Pan-Sharpening Method , 2012, IEEE Geoscience and Remote Sensing Letters.

[16]  Henry Leung,et al.  Fusion of Multispectral and Panchromatic Images Using a Restoration-Based Method , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Myeong-Ryong Nam,et al.  Fusion of multispectral and panchromatic Satellite images using the curvelet transform , 2005, IEEE Geoscience and Remote Sensing Letters.

[18]  Kishor P. Upla,et al.  Multiresolution fusion using contourlet transform based edge learning , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.

[19]  Kishor P. Upla,et al.  Pan-sharpening: Use of difference of Gaussians , 2014, 2014 IEEE Geoscience and Remote Sensing Symposium.

[20]  Steven K. Rogers,et al.  Perceptual-based image fusion for hyperspectral data , 1997, IEEE Trans. Geosci. Remote. Sens..

[21]  Liangpei Zhang,et al.  Adjustable Model-Based Fusion Method for Multispectral and Panchromatic Images , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[22]  Julien Radoux,et al.  Bayesian Data Fusion for Adaptable Image Pansharpening , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[23]  B. S. Manjunath,et al.  Multisensor Image Fusion Using the Wavelet Transform , 1995, CVGIP Graph. Model. Image Process..