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Feature-Level Fusion of Dual-Band Infrared Images Based on Gradient Pyramid Decomposition

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Feature-Level Fusion of Dual-Band Infrared Images Based on Gradient Pyramid Decomposition Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013) Feature-level Fusion of Dual-band Infrared Images Based on Gradient Pyramid Decomposition Xiujie Qu,Fu Zhang Ying Zhang School of Information and Electronics School of Information and Electronics Beijing Institute of Technology Beijing Institute of Technology Beijing, China Beijing, China e-mail: [email protected] Abstract—Infrared thermal imager has been widely used in the characterized by simple and intuitive, suitable for real-time fields of missile guidance and flaw detection. To identify the processing. However, at the same time, its essence is a target clearly, the advanced one adopts dual bands sensors to smoothing of the image processing. Therefore, the edges and capture images. Since of that, there is an urgent need of a the contours of the image will become blurred. Making fusion of the dual-bands images. The fused result includes matters worse, when the difference of the images is great, much more exhaustive information than any single one, and there will be traces of mosaic, which is not conductive to the can better reflect the actual. Among the algorithms used to human eye to identify [5]. fuse the dual-band infrared images, the weighted algorithm is Compared with the weighted algorithm, the Gradient the most widely used and easiest to be achieved. Nonetheless, pyramid decomposition is a kind of fusion algorithms based its effect is not desired. We extract the features of the source on the transformation domain. It uses 4 Gradient operators, images and make a fuse based on them on the feature-level. To get a better result, in this paper, the fusion strategy based on based on the Gaussian pyramid decomposition, to make a the Gradient pyramid transform has been mainly adopted. filtering in the horizontal, vertical, and two diagonal Meanwhile, there is a comparison with the weighted algorithm. directions. By this way, we can better extract the edge Also, it makes an evaluation and analysis to the experimental information of the source image, and keep the details of the data, and finally obtains the desired results. characteristics. The fused image has a better definition and contains enough effective messages. In this paper, we do a Keywordst—image fusion, feature extraction, Gradient comparative experiment of dual-bands infrared image fusion. pyramid decomposition, image reconstruction One group takes the Gradient pyramid algorithm, and the other is based on the weighted algorithm. Through making an analysis and evaluation of the experimental data, we have I. INTRODUCTION achieved the desired results. Infrared thermal imager has been widely used in the domains of missile guidance and flaw detection. Since II. WEIGHTED FUSION ALGORITHM putting the imager into operation, the single-band infrared Weighted average method is a weighted average detectors show some limitations in detecting targets. On the processing of two registration source images on the one hand, the target sometimes cannot be discovered by corresponding pixels [4]. Set A (x, y) and B (x, y) are the two medium wave infrared (MWIR) detector but can be easily pixels of source images, the fusion result is F(x, y), the detected by long wave infrared (LWIR) one. On the other process of weighted fusion can be expressed as: hand, the detection-range of MWIR is much longer then =+ωω LWIR in a humid environment [6]. To improve the detection Fxy(, )12 Axy (, ) Bxy (, ) (1) results, the advanced imager adopts sensors of dual bands to identify the target and extract the feature of it. Feature ωω+=1 (2) detection is a low-level image processing operation. That is, 12 it is usually performed as the first operation on an image, and examines every pixel to see if there is a feature present at x, y represent the row and column numbers of the image, ω ω that pixel. If this is part of a larger algorithm, then the 1 、 2 denote the weighted coefficient. In applications, algorithm will typically only examine the image in the region the weights can be obtained by principal component of the features [7]. The fused result includes much analysis: first step, calculate the covariance of two images exhaustive information than any single one that takes part in matrix: the fusion and can better reflect the actual [1,4]. Therefore, it can greatly enhance the definition of image and effectively ν c improve the success rate of detection of different classes of C = AAB (3) target. ν cAB B In the choice of dual-band infrared images fusion algorithm, the weighted algorithm is the most simple and commonly used one based on spatial domain. It is Published by Atlantis Press, Paris, France. © the authors 2279 Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013) and then calculate the eigenvalues of the covariance Here GPkl is the K-level and L-direction Gradient pyramid λλ、 matrix 12.After that, we can find out the largest (l=1,2,3,4 respectively mean the horizontal, vertical, two -1 = () diagonal directions), Gk is the Kth level image of Gaussian eigenvalue, , then the eigenvectors Xxx12,according • to the formula: ω pyramid, dl is the Lth direction gradient operator. is a core in size of 3*3. The filter operator can be defined as follows: λν−−c max AABX = 0 (4) −−λν − cAB max B 1 01 =−， = d1 [1 1] d2 2 10 Finally, we can get the nonzero solution. When using PCA method to determine the weighting coefficient, the part −1 1 −10 = ， = d3 d4 (8) of great gray gradient of the image will be enhanced. 1 2 01 Therefore, this method is of significant effect in processing • the images of huge difference in the gray gradient. However, ω this method is subject to the noise impact, especially the The is defined as: impulse noise. The gray scale mutations can even make the 121 fusion results worse. • ω = 1 242 (9) III. GRADIENT PYRAMID DECOMPOSITION 16 121 The Gradient pyramid decomposition is a kind of multi- resolution algorithm based on the Gaussian decomposition. Each level of the Gradient contains details on the The process steps are as follow: horizontal, vertical, and diagonal directions. Therefore, we can extract more clear and exhaustive message from the A. Establish Gaussian pyramid source image, and preserve the edge feature. In the image sequence of Gauss pyramid, through a low C. Reconstruction of the Gradient pyramid pass filter to the former one and then making a down The reconstruction of the Gradient pyramid is somewhat sampling, each level is obtained. complicated. The directed Laplacian pyramid and filter subtract decimate (FSD) Laplacian pyramid are generated as 22 the intermediate results during the process. The directed =+ω + Gijll(, ) ( mnG , )−1 (2 i m ,2 j m ) (5) Laplacian pyramid is defined as: mn=−22 =− → 1 L =− dGP* (10) kl 8 l kl In the formula,Gijl ( , ) denotes the L-level image of → Gaussian pyramid, G0 denotes the source image as the Lkl GPkl is the K-level and L-direction of the directed bottom of the pyramid. N is the level number and C means l Laplacian pyramid. Its accumulation forms FSD Laplacian the column number while R means the row number of the l pyramid L ： Lth level. ω(,)mn= hm ()() hn is a window function of low- k pass characteristics with 5*5 in size. h means Gauss density 4 → distribution function, we can calculate the value of h : = Lkkl L (11) l=1 3 1 1 h(0) = , hh(1)−=+= (1) , hh(2)−=+= (2) (6) 8 4 16 And the Laplacian is recovered from FSD pyramid: =−ω LPLkk[1 ]* (12) Finally, we get the sequences consists of GG01, ,..., GN [2,3]. Finally we can get the fused image by reconstructing the B. Establish Gradient pyramid Laplacian pyramid [2]. We can get the Gradient pyramid sequence by making a D. Steps of the image fusion algorithm four-gradient-direction filter of each level of the Gaussian • pyramid: Do Gaussian decomposition of each source image, • and establish the pyramid sequences. =+ω GPkl d l*[ G k * Gk ] (7) Published by Atlantis Press, Paris, France. © the authors 2280 Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013) • Establish the Gradient pyramid sequences based on Mathematically, the operator uses two 3×3 kernels which the Gaussian pyramid. are convolved with the original image to calculate • Fuse the pyramid decomposition layer with approximations of the derivatives - one for horizontal appropriate rule to obtain the fused image pyramid. changes, and one for vertical. If we define A as the source • G G Reconstruct the fused image pyramid, and then image, and x and y are two images which at each point obtain the final fused image. contain the horizontal and vertical derivative approximations, IV. FEATURE EXTRACTION the computations are as follows: In this paper, we take the sobel operator to extract the -1 0 1 12 1 features of the MWIR source image and the canny one to get GA=2 0 2* GA=0 0 0* (15) the features of the LWIR image. x y 10 1 -1 -2 -1 A. Canny operator The Canny edge detector is an edge detection operator that uses a multi-stage algorithm to detect a wide range of V. EVALUATION CRITERIA edges in images. The extraction results can have a clear A. Information entropy contour and prolific details of the target. The extraction steps of the canny operator are as follow: The concept of information entropy was proposed by a) Noise reduction: The image after a Gaussian mask Shannon, the famous scientist who was the founder of has been passed across each pixel.
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