Image Enhancement by Fusion in Contourlet Transform

Most existing image enhancement algorithms work on a single image. Their performance is limited to the capacity of the sensor by which the image is taken. In some cases they completely fail to provide us the necessary enhancements. This paper proposes a composite image approach for enhancing still images. The approach proposed combines the relevant features of the input images and produce a composite image which is rich in information content for human eye. The input images are first decomposed into multiple resolutions by using the contourlet transform which provides a better representation than the conventional transforms. Transformed coefficients are combined with a predefined fusion rules. The resultant image is found by performing inverse contourlet transformation of the composite image. The results found are encouraging and the algorithm does not introduce any distortion for applications in low light and/or non uniform lighting conditions. The composite image also contains almost all of the salient features of the input images

[1]  Li Tao Multi-Modal Enhancement Techniques for Visibility Improvement of Digital Images , 2005 .

[2]  Zhongliang Jing,et al.  Multi-focus image fusion using pulse coupled neural network , 2007, Pattern Recognit. Lett..

[3]  Marco Diani,et al.  Sight enhancement through video fusion in a surveillance system , 2007, 14th International Conference on Image Analysis and Processing (ICIAP 2007).

[4]  Fionn Murtagh,et al.  Gray and color image contrast enhancement by the curvelet transform , 2003, IEEE Trans. Image Process..

[5]  Alexander Toet,et al.  Image fusion by a ration of low-pass pyramid , 1989, Pattern Recognit. Lett..

[6]  Zia-ur Rahman,et al.  Retinex processing for automatic image enhancement , 2004, J. Electronic Imaging.

[7]  Michael F. Cohen,et al.  Digital photography with flash and no-flash image pairs , 2004, ACM Trans. Graph..

[8]  Vijayan K. Asari,et al.  A multisensor image fusion and enhancement system for assisting drivers in poor lighting conditions , 2005, 34th Applied Imagery and Pattern Recognition Workshop (AIPR'05).

[9]  Gang Liu,et al.  Pixel-Level image Fusion Based on Fuzzy Theory , 2007, 2007 International Conference on Machine Learning and Cybernetics.

[10]  Koen Van de Velde Multi-scale color image enhancement , 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).

[11]  Vijanth S. Asirvadam,et al.  Multi-Sensor Image Enhancement and Fusion for Vision Clarity Using Contourlet Transform , 2009, 2009 International Conference on Information Management and Engineering.

[12]  Zia-ur Rahman,et al.  Retinex processing for automatic image enhancement , 2002, IS&T/SPIE Electronic Imaging.

[13]  Minh N. Do,et al.  Ieee Transactions on Image Processing the Contourlet Transform: an Efficient Directional Multiresolution Image Representation , 2022 .

[14]  Gemma Piella,et al.  Adaptive wavelets and their applications to image fusion and compression , 2003 .

[15]  Peter J. Burt,et al.  Enhanced image capture through fusion , 1993, 1993 (4th) International Conference on Computer Vision.