Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Combining Bilateral Filtering and Fusion of Visual and IR Images


Affiliations
1 Department of Computer Science, Erode Arts and Science College, Erode, Tamil Nadu, India
     

   Subscribe/Renew Journal


This paper presents efficient fusion algorithms to discover hidden objects in the visual scene bymeans of merging visual and infrared images of the same scene. Generally, images are corrupted by noise. It is highly difficult to discover the objects in the corrupted image due to different types of noise appearing in the images. To remove noise while preserving edges in noisy input images, bilateral filter is proposedin this paper. Most popular fusion techniques including average and condition rule is employed to obtain a complement fused image from the noisy source images. Along with these two fusion rules, four algorithms have been generated with bilateral filter for finding hidden objects. First, the visual and IR sources degraded by noise are smoothed by bilateral filter. Second, both IR and visual-denoised images are fused by applying one of the proposed pixel-level techniques. The proposed algorithms are tested over four sets of visual and IR images to find out objects that are having worse background as smoke, illumination and bad weather climate. Experiments have been carried out and results were obtained.

Keywords

Image Fusion, Bilateral Filter, Object Detection, Hidden Objects, IR Image, Multisensor.
Subscription Login to verify subscription
User
Notifications
Font Size


  • Aslantas, V. & Kurban, R. (2009). A comparison of criterion functions for fusion of multi-focus noisy images. Optics Communication, 282(16), 3231-3242.
  • Broggi, A., Cerri, P., Ghidoni, S., Grisleri, P. & Gi, H. (2009). A New Approach to Urban Pedestrian Detection for Automatic Braking. IEEE Transactions on Intelligent Transportation Systems, 10(4), 594-605.
  • Chang, H. (2010). Entropy-based Trilateral Filtering for Noise Removal in Digital Images. 2010 3rd International Congress on Image and Signal Processing (CISP2010), 2, 673-677.
  • Dalai, N. & Triggs, B. (2005). Histograms of Oriented Gradients for Human Detection. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1, 886-893.
  • Dalai, N., Triggs, B. & Schmid, C. (2006). Human Detection using Oriented Histograms of Floe and Appearance. Proceedings of the 9th European Conference on Computer Vision, Part II, 428-441.
  • Daneshvar, S. & Ghassemian, H. (2010). MRI and PET image fusion by combining IHS and retina-inspired models. Information Fusion, 11(2), 114-123.
  • Doshi, N. P., Schaefer, G & Merla, A. (2012). An Evaluation of Image Enhancement Techniques for Capillary Imaging. Proceedings of the IEEE International Conference on System, Man and Cybernetics (SMC), 1428-1432.
  • Goshtasby, A. A. & Nikolov, S. (2007). Image fusion: Advances in the state of the art. Information Fusion, 8(2), 114-118.
  • Haghighat, M. B. A., Aghagolzadeh, A. & Seyedarabi, H. (2011). A non-reference image fusion metric based on mutual information of image features. Computers and Electrical Engineering, 37(5), 744-756.
  • Han, J. & Bhanu, B. (2007). Fusion of color and infrared video for moving human detection. Pattern Recognition, 40(6), 1771-1784.
  • Hu, J. & Li, S. (2012). The multi-scale directional bilateral filter and its application to multi-sensory image fusion. Information Fusion, 13(3), 196-206.
  • Hu, J. & Li, S. (2011). Fusion of Panchromatic and Multi-spectral Images Using Multi-Scale Dual Bilateral Filter. 18th IEEE International Conference on Image Processing (ICIP), (pp. 1489-1492).
  • Krebs, W. K., Scribner, D. A., Miller, G. M., Ogawa, J. S. & Schuler, J. (1998). Sensor Fusion: Architectures, Algorithms, and Applications II. SPIE - International Society for Optical Engineering, 2, 129-140. Bellingham, WA.
  • Lagendijk, R. L., Biemond, J. & Boekee, D. E. (1988). Regularized Iterative Image Restoration with Ringing Reduction. IEEE Transactions on Acoustics, Speech and Signal Processing, 36(12), 1874-1887.
  • Li, H., Manjunath, S. & Mitra, S. (1995). Multi-sensor image fusion using the wavelet transform. Graphical Models and Image Processing, 57(3), 235-245.
  • Li, M., Cai, W. & Tan, Z. (2006). A region-based multi-sensor image fusion scheme using pulse-coupled neural network. Pattern Recognition Letters, 27(16), 1948-1956,
  • Li, H., Chai, Y, Yin, H. & Liu, G. (2012). Multi-focus image fusion and de-noising scheme based on homogeneity similarity. Optics Communications, 285(2), 91-100.
  • Lin, Z. & Shi, Q. (1999). An Anisotropic Diffusion PDE for Noise Reduction and Thin Edge Preservation. Proceedings of the 10th International Conference on Image Analysis and Processing. (pp. 102-107).
  • Malviya, A. & Bhirud, S. G (2010). Visual infrared video fusion for night vision using background estimation. Journal of Computing, 2(4), 66-69.
  • Matsopoulos, G K. & Marshall, S. (1995). Application of morphological pyramids: Fusion of MR & CT phantoms. Journal of Visual Communication and Image Representations, 6(2), 196-207.
  • Mikolajczyk, K., Schmid, C. & Zisserman, A. (2004). Human Detection based on Probabilistic Assembly of Robust Part Detectors. European Conference on Computer Vision, Lecturer notes in Computer Science, 3021, 69-82.
  • Muller, A. C. & Narayanan, S. (2009). Cognitively engineered multi-sensor image fusion for military applications. Information Fusion, 10(2), 137-149.
  • Paris, S., Kornprobst, P., Tumblin, J. & Durand, F. (2008). Bilateral Filtering: Theory and Applications. Foundations and Trends in Computer Graphics and Vision, 4(1), 1-73.
  • Piella, G (2003). A general framework for multi-resolution image fusion: From pixels to regions. Information Fusion, 4(4), 259-280.
  • Riley, T. & Smith, M. (2006). Image Fusion Technology for Security and Surveillance Applications. Proceedings of SPIE 6402, Optics and Photonics for Counter terrorism and Crime Fighting II, 6402.
  • Siddiqui, A. B., Jaffar, M. A., Hussain, A. & Mirza, A. M. (2010). Block-based Feature-level Multi-focus Image Fusion. 5th International Conference on Future Information Technology.
  • Simone, G, Farina, A., Morabito, F. C, Serpico, S. B. & Bruzzone, L. (2002). Image fusion techniques for remote sensing applications. Information Fusion, 3(1), 3-15.
  • Sochen, N, Kimmel, R. & Malladi, R. (1998). A Geometrical Framework for Low Level Vision. IEEE Transactions on Image Processing, 7(3), 310-318.
  • Sun, Z. H., Bebis, G. & Miler, R. (2006). On-Road Vehicle Detection: A Review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(5), 694-710.
  • Teot, A. (1989). Image fusion by a ratio of low-pass pyramid. Pattern Recognition Letters, 9, 245-253.
  • Toet, A. & Walraven, J. (1996). New false color mapping for image fusion. Optical Engineering, 35(3), 650-658.
  • Toet, A. & Franken, M. (2003). Perceptual evaluation of different image fusion schemes, Displays, 24(1), 25-37.
  • Toet, A., Hogervorst, M. A., Nikolov, S. G, Lewis, J. J., Dixon, T. D., & Bull, D. R. (2010). Towards cognitive image fusion. Information Fusion, 11(2), 95-113.
  • Tomasi, C. & Manduchi, R. (1998). Bilateral Filtering for Gray and Color Images. 6th IEEE International Conference on Computer Vision. (pp. 839-846).
  • Xu, F., Liu, X. & Fujimura, K. (2005). Pedestrian Detection and Tracking with Night Vision. IEEE Transactions on Intelligent Transportation Systems, 6(4), 63-71.

Abstract Views: 338

PDF Views: 0




  • Combining Bilateral Filtering and Fusion of Visual and IR Images

Abstract Views: 338  |  PDF Views: 0

Authors

Shanmugasundaram Marappan
Department of Computer Science, Erode Arts and Science College, Erode, Tamil Nadu, India

Abstract


This paper presents efficient fusion algorithms to discover hidden objects in the visual scene bymeans of merging visual and infrared images of the same scene. Generally, images are corrupted by noise. It is highly difficult to discover the objects in the corrupted image due to different types of noise appearing in the images. To remove noise while preserving edges in noisy input images, bilateral filter is proposedin this paper. Most popular fusion techniques including average and condition rule is employed to obtain a complement fused image from the noisy source images. Along with these two fusion rules, four algorithms have been generated with bilateral filter for finding hidden objects. First, the visual and IR sources degraded by noise are smoothed by bilateral filter. Second, both IR and visual-denoised images are fused by applying one of the proposed pixel-level techniques. The proposed algorithms are tested over four sets of visual and IR images to find out objects that are having worse background as smoke, illumination and bad weather climate. Experiments have been carried out and results were obtained.

Keywords


Image Fusion, Bilateral Filter, Object Detection, Hidden Objects, IR Image, Multisensor.

References