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Gagandeep Kour1, Sharad P. Singh2
  1. M. Tech Student, Department of EEE, Arni University, Kathgarh, Himachal Pardesh, India-176401
  2. Assistant Professor, Department of EEE, Arni University, Kathgarh, Himachal Pardesh, India-176401
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Image fusion is implemented on blurred images using two techniques SVD and DWT. Computer based algorithm is design for SVD and DWT for enhancing the images. A low pixel value images were captured using an optical sensor. These images were blurred using circular averaging filter with different averaging value ranging from 1- 5. Parameters such as peak signal to noise ratio (PSNR), root mean square error (RMSE), signal to noise ratio (SNR), and universal image quality index (UIQI) for blurred, SVD, and DWT fused images. From the computed values of UIQI SVD shows maximum value with respect to DWT fusion techniques.


SVD, DWT, PSNR, SNR, UIQI, Image fusion


Processes of combining multiple images of same scene by implementing computer based algorithm to enhance the information content in the image are being developed and known as image fusion [1]. The image fusion techniques help in improving the quality of images. Image fusion finds its application in the field of optical remote sensing; CCD devices with limited focusing, medical image enhancement [2]. The image fused may be captured from different optical sensor, acquired at different spatial time with different spectral characteristics. Image fusion is being developed with one objective to retain the desirable characteristics of the image. Nowadays a wide range of optical sensors are available with multi resolution data, this lead to increasing interest of researchers in the field image fusion for wide spectrum applications [3]. In order to carryout image fusion various algorithm are being developed to provide good result. Various techniques such as wavelet, DCT, SVD etc are being explored to enhance the image contents after image fusion is applied on the set of data.
In this research paper information set of the data taken from optical sensor and image fusion is applied to achieve better set image with the help of Discrete Wavelet Transformation (DWT) and Singular Value Decomposition (SVD) based image fusion. Also the effectiveness of these techniques are evaluated by comparing various content parameters.


basic principle used in linear algebra for SVD is the factorization of rectangular real or complex matrix into diagonal symmetric or Hermitian square matrices using eigenvectors [4]. SVD of an m*nmatrix X is given by:


Investigation is carried out to estimate the parametric features of image after applying SVD and DWT based image fusion. Information content of the distorted image is enhanced with these two techniques. Set of images are taken using standard optical camera and are stored for further processing. A computer based algorithm is designed with implementation of image fusion using SVD and DWT technique. Before applying SVD and DWT based image fusion, circular averaging filter was applied to these images to add some distortion. Block diagram of the algorithm designed is shown in Fig. 1.
After applying the SVD and DWT image fusion on the distorted image, information content of the image were calculated which are described as -
Root mean square error (RMSE) corresponds to pixels in the reference image r I and the fused image f I . If the reference image and fused image are alike give the RMSE value equal to zero and it will increase when the dissimilarity increases between the reference and fused image [9].
image (5)
Peak signal to noise ratio (PSNR) value will be high when the fused and reference images are alike and higher value implies better fusion. PSNR is calculated by follow equation [10] [11]
image (6)
Signal to noise ratio (SNR) is calculated using following [12]


The performance of proposed method of SVD and DWT image fusion is tested using different levels of distorted images. Circular averaging filter is implemented to blur the image with the level ranging from 1-5. Figure 2 shows the blurred image with corresponding enhance image obtained from the designed SVD algorithm of image fusion. Figure 2 (a) to 2(e) show the blurred images with the application of circular averaging filter having radius from 1 to 5. Corresponding enhanced images with SVD fusion are shown in Fig. 2(f)-2(j). Similarly Fig 3(a)-3(e) shows the blur image with DWT fused image shown in 3(f)-3(j). The calculated image content information (such as PSNR, SNR, RMSE, universal image quality index (UIQI) of blurred image with respect to reference image for blurred image are given in Table 1. The quality parameters of the image after the application of the SVD and DWT image fusion are also calculated shown in Table 2 and Table 3.


A computer based algorithm is developed for SVD and DWT based image fusion. Various levels of blurring levels of the images were taken using circular averaging filter. SVD showed its dominance over DWT with respect to UIQI having values for blurring level 1 to 5. The values of UIQI for level 1 to 5 are 0.0153, 0.0155, 0.0164, 0.0165, and 0.0154 respectively. Similarly UIQI values for DWT based fusion were 0.0091, 0.0094, 0.0092, 0.0093, and 0.0093 for level 1 to 5 respectively.