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RETRACTED ARTICLE: A method for spectral image registration based on feature maximum submatrix
EURASIP Journal on Image and Video Processing volume 2018, Article number: 140 (2018)
Abstract
In order to solve the geometric offset caused by replacing the filter during imaging of multichannel spectral image data, a multichannel spectral image registration method based on SURF feature merged with maximum submatrix is proposed. Firstly, the feature of multichannel spectral image is extracted by SURF. Then, a perspective transformation is performed to obtain a preliminary registration image. For the problem of invalid areas with zero pixel values appearing at the boundary of the image after registration, the largest submatrix is used to detect the maximum inscribed rectangle of the image to remove the invalid boundary region, and maximize the reserved effective region information. Experimented was processed with multichannel imaging data of murals. Experimental results agree with the theoretic analysis and verified that the proposed registration method is better adapted to image scale and brightness changing. At the same time, it can avoid the influence of invalid regions generated by other registration methods on subsequent spectral reconstruction and color restoration which has better performance.
1 Introduction
Multichannel spectral imaging equipment needs to replace the filter to adjust the imaging channel parameters during data acquisition [1]. The operation of replacing the filter will cause the position of the multispectral camera to shift which resulting in geometric distortion and offset between the multichannel images. This situation will affect the subsequent spectral image pixel analysis and spectral reconstruction [2]. Therefore, it is necessary to perform registration preprocessing correction on multichannel spectral images.
Accurate image registration is a necessary preprocessing step for applications such as image fusion, target detection, and spectral reconstruction [3,4,5]. Common registration methods mainly include registrationbased region and registrationbased feature. The registrationbased region method is sensitive to grayscale changes and difficult to process images with weak gray correlation. And its computational complexity is relatively low. The registrationbased feature method is relatively small in computation. It also has good adaptability to changes in image offset and rotation. This method has gradually become the mainstream of image registration.
The key to registrationbased feature is to find a better feature description method and feature matching algorithm. Currently, common feature point extraction methods are Harris feature extraction [6], scale invariant feature transform (SIFT) [7, 8], and smallest univalve segment assimilating nucleus (SUSAN) [9]. The SIFT proposed by D.G. Lowe has been widely concerned by scholars because of its invariance to illumination, rotation, scale, and other transformations. However, SIFT requires a 128dimensional vector feature operation with a large amount of computation. The speeded up robust features (SURF) [10] proposed by Herbert Bay [11] simplifies the operation of image pyramid decomposition based on SIFT. SURF is also a scale and rotation invariant feature description method which approximates the convolution of the image. The concept of the integral map introduced by SURF in the feature point positioning step greatly reduces the computational complexity of solving the Hessian matrix, and the computation time is reduced by more than three times compared with SIFT.
Multichannel spectral camera imaging is a grayscale image. Each channel reflects the spectral characteristics of different bands. And the gray value of the image changes significantly. SURF method can extract feature points better when the brightness changing greatly [12] which can better adapt to the characteristics of the spectral image. However, the multichannel spectral image after registration using the SURF method results in an invalid region with a pixel value of zero in the boundary region, which brings computational errors to subsequent spectral reflectance reconstruction and color reproduction [13] based on multichannel images. Regarding the issue above, a multichannel spectral image registration method based on SURF feature fusion maximum submatrix is proposed in this paper. This method firstly uses SURF to perform preliminary registration on multichannel spectral images. Then, the inscribed rectangle detection method based on maximum submatrix is used to segment the effective information area in the preliminary registration result. The information of irregularly aligned registration results is maximize retained, which can get a final higher precision registration result.
2 Method
2.1 SURF feature extraction
The SURF consists of two main parts: feature point detection and location and generating feature point description operator.

1)
Feature point detection and location
The Hessian matrix HM(x, σ) is the core of the SURF. In mathematics, the Hessian matrix is a square matrix of secondorder partial derivatives of a multivariate function, describing the local curvature of the function. Before SURF detects feature points, the integral image I is firstly calculated. Then, feature point selection and scale transformation have been done by using the Hessian matrix of I. The value at any pixel p = (x, y) in image I is the sum of the gray values of the corresponding rectangular area from the upper left corner of the original image to any point. Its mathematical formula is as follows:
On scale σ, the Hessian matrix HM(x, σ) of point p is defined as follows:
In HM(x, σ), x indicates feature point coordinates, σ indicates scale. L _{xx}(x, σ),L _{xy}(x, σ), and L _{yy}(x, σ) is the convolution of image I at point P and Gaussian secondorder partial derivative \( \frac{\partial^2g\left(\sigma \right)}{\partial {x}^2} \). g(σ) is a Gaussian function as shown below:
To simplify the calculation, SURF approximates the secondorder Gaussian filter with a box filter which increases speed while maintaining performance. Take 9 × 9 box filter as an example, taking the scale σ = 1.2, the Gaussian secondorder partial derivative can be approximated as shown in Fig. 1.
Assuming that the parameters in the Hessian matrix obtained by convolving the image with the above box filter are D _{xx}, D _{xy}, and D _{yy}, respectively, the matrix in Eq. (2) can be approximated as:
where w is the box filter weight coefficient. When σ = 1.2, w can be approximated to 0.9. Establishing scale space, in the 3 × 3 × 3 domain of the scale space, the value of each point is compared with the adjacent position of the current scale and 26 fields around the adjacent scale, and the local maximum point is obtained. Through the interpolation calculation, the final feature points in the continuous space can be obtained.

2)
Generating feature point description operator
In order to ensure the rotation invariance of the feature points, it is necessary to assign a main direction to the feature points. The Haar wavelet response is calculated in the x and y directions of each point in a circular region centered on the feature point and having a radius of 6 times. These responses are given Gaussian weights. Adding the horizontal response d _{x} and the vertical response d _{y} in the sector area w every 60° to obtain the local direction vector (m _{w}, θ _{w}).
where m _{w} is the sum of horizontal and vertical Haar wavelet features for all points in the w sector. θ _{w} is the local direction angle of Haar wavelet feature in the sector of w. Compare all local direction vectors (m _{w}, θ _{w}) and use the longest vector θ as the main direction of the feature points as follows:
The second step of generating a feature point description operator is to create a feature descriptor for the feature point. After obtaining the main direction of the feature point, the main direction is the x axis, and the rectangular area of 20s × 20s is selected in the neighborhood around the feature point. The Haar wavelet response value of the pixel points in each subdomain by dividing the rectangular area into 16 subdomains is calculated. ∑d _{x}, ∑ ∣ d _{x}∣, ∑d _{y}, and ∑ ∣ d _{y}∣ are separately counted to form a feature vector v = (∑d _{x}, ∑  d _{y}, ∑  d _{x} , ∑  d _{y} ). Each feature vector has 4 dimensions with a total of 16 subfields, so a 64dimensional feature point description operator is finally obtained.

3)
Feature point matching
After the feature description operator is generated, the similarity matching is performed by using the feature description operator of the original image and the target image. The Hessian matrix traces corresponding to the feature points with the same contrast are the same number, and the Hessian matrix traces corresponding to different contrasts are different numbers. Firstly, the Hessian matrix trace is used to perform the preliminary matching of the feature points, and the feature pairs of the same number are selected. Secondly, the Euclidean distance matching method is used to judge the similarity of the selected feature pairs, and the onetoone correspondence between the target image and the feature points to be matched in the source image is found. Assuming that (x _{1}, x _{2}…, x _{N}), \( \left({x}_1^{\prime },{x}_2^{\prime}\dots, {x}_N^{\prime}\right) \) is a pair of matching feature vectors, the Euclidean distance D between the two vectors is calculated as follows:
The Euclidean distance of all the feature points to be matched on the target image and the source image are calculated. The minimum Euclidean distance D _{near} and the next smallest Euclidean distance D _{sub _ near} are selected. Let \( \eta =\frac{D_{\mathrm{near}}}{D_{\mathrm{sub}\_\mathrm{near}}} \), comparing η with threshold T, if η < T, the feature point on the target image matches the corresponding point on the source image D _{near}. Otherwise, it does not match.

4)
Perspective transformation
According to the matching point, the coordinate relationship between the source image and the target image, that is, the perspective transformation matrix H between the two images, is as follows:
If p = (u, v), q = (x, y) is feature point pair for matching, the projection transformation formula will be as follows:
where (u, v) is the original image coordinates and \( \left(\frac{x}{w},\frac{y}{w}\right) \) is the target image after the transformation. Each parameters h _{i}(i = 0, 1, 2, …, 7) in H can be calculated from Eq. (9). The perspective transformation is used to complete the registration of the original image to the target image.
2.2 Spectral image registration based on SURF features
SURF feature extraction and perspective transformation are used to registration of multichannel spectral images. The spectral image of one channel is taken as the target image, and the transformation matrix H of the other channel spectral image to the target image is sequentially calculated. But there are still a large number of erroneous matching points in the feature points obtained through preliminary matching. In order to ensure the calculation accuracy of the perspective transformation model, the error matching points should be eliminated as much as possible to ensure the quality and effect of image registration. The random sample consensus (RANSAC) [14] method is used to filter feature points. The steps to use RANSAC are as follows.
Step 1: Four sets of feature point pairs are randomly extracted from the preliminary matching set P. The transformation matrix H is calculated and recorded as the model M.
Step 2: The projection error of all data in P with model M is calculated. If the result is less than the threshold value t, add the inner point set I and record the statistical error errormin under the model.
Step 3: The above steps are repeated. When calculating a new model, the statistical error error is compared with the errormin size. If the error is smaller, update the model M and errormin.
Step 4: The optimal model M of maximum inner point set I is output.
The above four sets of feature point pairs are taken in step 1 because the geometric transformation model is selected as the perspective transformation model. There are eight unknowns in the model, and at least eight sets of linear equations are needed to solve. A set of feature point pairs can list two equations, so four sets of feature point pairs are selected. When the threshold is set to t = 0.7, a better matching correct rate can be obtained [15]. The optimal model M can be obtained in each parameter h _{i}(i = 0, 1, 2, …, 7).
After the perspective transformation, the registered multichannel spectral image can be obtained. As can be seen from Eq. (9), the perspective transformation registration is as follows:
where (x ^{′}, y ^{′}) is the coordinate of the pixel point (u, v) on the original image converted to the target image by the Eq. (10). The effect of registering the spectral images of any three channels using SURF is shown in Fig. 2. Target is the target image. Channel 1, channel 2, and channel 3 are the images to be registered.
As can be seen from Fig. 2, because of perspective transformation, there are different degrees of black borders on the boundaries of each channel image after SURF registration. Superimposing all channels, the resulting spectral cube image has irregular boundaries. Such spectral cube image data can have error effects on later pixel analysis, spectral reconstruction, and color reproduction. The irregular boundaries need to be intercepted.
2.3 Maximum inscribed rectangle detection
The treatment of irregular boundaries is mainly to solve the problem of the maximum enclosed rectangle (MER) [16,17,18] of the target object. After obtaining the minimum circumscribed rectangle of the target object, the largest inscribed rectangle of the object in the minimum circumscribed rectangle is found to obtain the maximum inscribed rectangle area. At present, the commonly used methods for obtaining the maximum inscribed rectangle are the traversal method and the central diffusion method [19].

1)
Traversal method
Given a coordinate point, all rectangular areas are calculating with the point as the upper right corner within the target object. Compare the rectangular area to get the largest area rectangle with the point as the upper right corner. The maximum inscribed rectangle of the target object can be obtained by calling this method at every point in the target object.

2)
Central diffusion method
The minimum circumscribed rectangle center point o(x, y) is calculating as follows:
where left and up are the vertices and ordinates of the upper left corner of the minimum circumscribed rectangle, respectively, right and down are the ordinate and ordinate of the lower right corner of the minimum circumscribed rectangle, respectively. Traversing up − 1 rows on interval (left − 1, right + 1), whether the pixel is 0 is determined. If the current line pixel is not 0, move the up − 1 line upward, that is up − 2; otherwise, stop moving. Traversing down + 1 rows on interval (left − 1, right + 1), if the current row pixel value is not 0, move the down + 1 line upward, that is down + 2; otherwise, stop moving; traversing right and left in the same way on interval (up − 1, down + 1). When left, right, up, and down stop moving, the enclosed area is the largest inscribed rectangular area.
2.4 Spectral image registration based on proposed MSMSURF
Aiming at the problems of SURF preliminary registration and maximum inscribed rectangle detection methods, this paper proposes a MSMSURF (maximum submatrixSURF) method that combines SURF feature extraction with the maximum submatrix to achieve more efficient multichannel spectral image registration. The algorithm flow chart of MSMSURF is shown as in Fig. 3.
The n channel spectral images M _{q}(q = 1, 2, …, n) after the SURF preliminary registration are superimposed to obtain a matrix I. The superposition is as follows:
where i, j respectively represent the rows and columns of the matrix M _{q}, m _{k, ij} is the pixel value of the spectral image matrix M _{k} at i, and j of the kth channel after preliminary registration. The effect of the matrix I obtained by superimposing the spectral image matrix M _{q} of the three channels is shown as Fig. 4.
As can be seen from Fig. 4, the superposition matrix obtained after SURF preliminary registration makes the invalid areas of the boundaries of each channel image cumulatively superimposed. The boundary of the image cube is irregular. For the binarization of the superposition matrix I, the maximum inscribed rectangle detection is performed. For the problems in the traversal method and the central diffusion method, this paper proposes the maximum submatrix (MSM) method for maximum inscribed rectangle detection. The main idea of the MSM method is that, in a given matrix, assuming that the matrix element contains only 0 and 1, all submatrices that do not contain any 0 elements have been found. The area of all submatrices is calculated. The submatrix with the largest area is the MSM.
The MSM method mainly includes the following steps:
Step 1: Set the value of the point in matrix I whose value is greater than the threshold Q to 1, and the remaining points are all 0. The target matrix I is binarized. Since the multichannel spectral images are grayscale, the pixel value of the boundary invalid region is 0 after the perspective transformation. So set the threshold Q to 0. Binarization matrix I is as follows:
Step 2: Matrix N with the same size as matrix I is created. Initializing N, let N _{1j=} I _{1j}. When i > 1, if I _{ij} ≠ 0_{,}then N _{ij} = I _{ij} + I _{(i1)j}. N _{ij} = I _{(i1)j}; otherwise, N _{ij} = 0.
Step 3: Array arr[row] is created. And row is the number of columns of the matrix N. Array raw data is stored in arr. Traversing array arr is as follows:
a. If i = 0, the stack is empty at this time, put the array index i = 0 onto the stack. Else if i ≠ 0, get the top element of the stack firstly. When arr[i] ≥ arr[pop], push i onto the stack.
b. If arr[i] < arr[pop], pop up the top element pop, let h = arr[pop], w = pop − pop _{next} − 1 where pop _{next} is the new top element after p popping. Let S _{k} = h × w, repeat step a until the array traversal is complete.
c. Compare the S _{k} size and find the maximum value S _{max} = {k = 1, 2, …, n max(S _{k})}.
Step 4: Traversing matrix N by row, let arr[row] = N _{i}. Repeat step 3 and get every line S _{max}. The area enclosed by S _{max} in the ith row is the largest submatrix MaxS _{i} in the first row to ith row of the matrix I. Let P = {i = 1, 2, …, n max(MaxS _{i})}, Pis the MSM of matrix I.
The calculated MSM is shown as Fig. 5.
The MSM is used to intercept the spectral image of each channel after SURF preliminary registration, and the MSMSURF method registration result of each channel is obtained as shown in Fig. 6.
As can be seen from Fig. 6, MSMSURF method uses SURF feature extraction and projection transformation to initially register multichannel spectral images. Then, for the invalid region where the image boundary pixel value of the initial registration is 0, the MSM is used for interception. MSMSURF method maximizes the retention of valid area information while intercepting invalid regions of each channel spectral image, which improves the effect of registration.
3 Experimental results
3.1 Experimental data
Spectral images of six channels of three sets of murals were used as experimental data. The acquisition system includes Ocean Optics’ SpectroCam VIS model CCD multispectral camera, six narrowband interference filters and CIE standard A illumination source. The spectral images of each set of murals in six different band channels are collected as shown in Fig. 7.
In Fig. 7, the spectral images of each channel in each group of murals have different degrees of offset geometric distortion, and registration correction is needed.
3.2 Evaluation standard
In order to test the registration performance of methods, four criteria, feature point accuracy [20], registration accuracy [21], effective area pixel percentage [22], and operational time efficiency [23, 24] are used for objective evaluation.
Registration accuracy is determined by the root mean square error (RMSE) of the coordinates between all matching feature point pairs of the source image and the target image.
where (x ^{′}, y ^{′}) is the feature point coordinate on the target image, (x, y) is the coordinate of the feature point on the image to be registered, f represents coordinate transformation relationship, and n is the number of matching feature points after screening.
Feature point accuracy K is the ratio of the number of correctly matched feature point pairs to the number of all matching feature point pairs.
where R is the final correct matching feature point pair number, N is the total number of matching feature point pairs.
Effective area pixel percentage Rate is the ratio of the number of pixels included in the region extracted by the largest inscribed rectangle to the number of pixels in the entire image.
where c is the maximum inscribed rectangle containing the number of pixels and s is the number of pixels in the entire image.
3.3 Experimental results
The experiment firstly compares the time efficiency of SIFT and SURF merged with maximum inscribed rectangle detection of MSM. The experimental data of murals A, B, and C is respectively processed with MSMSIFT and MSMSURF methods. The effect of the methods is compared as shown in the following Table 1.
The data of the murals A, B, and C are respectively processed by traversing merged with SURF (TSURF), center diffusion merged with SURF (CDSURF), and MSMSURF algorithms. The detection interception results of the three methods for the superposition matrix after initial registration are shown in the Fig. 8.
Quantitative analysis of the above detection interception results are performed by RMSE, K, time, and rate criterion as shown in Table 2.
4 Discussion
The abilities of each registration methods for multichannel spectral images discussed in this paper are compared in this section. The scale invariance of SURF is better than Harris. The time complexity of SURF is lower than SIFT. And SURF is more robust to the image brightness changing. In the experiment, the perspective transformation of SIFT and SURF for feature point detection and matching are firstly compared. In order to get better effect of maximum inscribed rectangle detection, both SURF and SIFT are merged with MSM. It can be seen from Table 1 that compared with the MSMSIFT, the registration accuracy of MSMSURF is a little better than MSMSIFT. The value of RMSE is reduced by about 0.3. The feature point accuracy of MSMSURF is much better than MSMSIFT. The value of K is increased by an average of 21.5%. At the same time, due to the concept of introducing integral images, the runtime of SURF is greatly reduced by about 60% of SIFT. The result of Table 1 shows that the overall performance of SURF is better than SITF. Therefore, the subsequent experiments mainly focus on SURF merging with different inscribed rectangle detection methods.
The abilities of TSURF, CDSURF, and MSMSURF that SURF merged with different maximum inscribed rectangle detection methods of traversal, central diffusion, and maximum submatrix are compared. In the practical situation, the traversal method has strong robustness, but with high complexity which is not suitable for large area target object detection. The central diffusion method has low complexity, but with sensitive changing to irregular object edges which leads to low detection accuracy. It can be seen from Fig. 8 that when the image offset after perspective transformation is a large amount, TSURF and MSMSURF all retain the same maximum information for the detection interception results of the superposition matrix. But when the image offset is a small amount, only MSMSURF retains the maximum information. MSMSURF has better adaptability. Table 2 shows that since the three methods use SURF, the registration accuracy of RMSE is almost the same as feature point matching accuracy. Due to the effective area that can be retained by MSM after interception, MSMSURF has a slightly higher effective ratio than TSURF. At the same time, the effective ratio of CDSURF is only 60% of MSMSURF. CDSURF and MSMSURF are similar in runtime. Due to the larger amount of global traversal calculation of traversal method, the complexity of TSURF is much higher than CDSURF and MSMSURF. The time efficiency of TSURF is about three times that of CDSURF and MSMSURF. Based on the above analysis, MSMSURF not only has a higher effective ratio of detection, but also has lower algorithm time complexity, which is more suitable for registration of multichannel spectral images.
5 Conclusions
In this paper, a MSMSURF method merged SURF features with maximum submatrix is proposed based on the characteristics of multichannel spectral images, which improves the defects of existing matching and inscribed rectangle detection methods. Experiment with the multichannel spectral image data of the actual murals was processed. Experimental results show that MSMSURF can effectively combine SURF features with maximum submatrix detection. MSMSURF can solve the problem of invalid boundary regions in SURF feature registration and can maximize the effective region information. It has a good practical significance for the registration of multichannel spectral images.
Change history
13 September 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1186/s13640022005937
Abbreviations
 CDSURF:

Center diffusion  SURF
 MER:

Maximum enclosed rectangle
 MSM:

Maximum submatrix
 MSMSURF:

Maximum submatrix  speeded up robust features
 RANSAC:

Random sample consensus
 RMSE:

Root mean square error
 SIFT:

Scale invariant feature transform
 SURF:

Speeded up robust features
 SUSAN:

Smallest univalve segment assimilating nucleus
 TSURF:

Traversing  SURF
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Acknowledgements
The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.
Funding
This work has been supported in part by a grant from the National Natural Science Foundation of China (no. 61701388), the Natural Science Foundation of Shaanxi (nos. 2018JM5127 and no. 2018JM6080), the International Cooperation Funding Project of Shaanxi Provincial Science and Technology Department (no. 2017KW036), the Think Tank Project of Shaanxi Provincial Department of Education Key Scientific Research Plan (no. 18JT006), and the Soft Science Project of Science and Technology Bureau of Xi’an (no. 2016043SF/RK06(3)).
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About the authors’
Ke Wang received his master’s degree from Southwest Jiaotong University, Chengdu, China, in 2007. Currently, he is a Ph.D. candidate in School of Management at Xi’an University of Architecture and Technology. He is an instructor in School of Information and Control Engineering at Xi’an University of Architecture and Technology. His research interests include image processing, computational intelligence, and machine learning.
Huiqin Wang received her Ph.D. degree from Xi’an Jiaotong University, Xi’an, China, in 2002. She engaged in postdoctoral research work in Xi’an Jiaotong University from 2002 to 2004. Currently, she is a professor in School of Information and Control Engineering at Xi’an University of Architecture and Technology. Her research interests include multimedia information security, digital image processing, information management, and information system.
Meng Wu received her Ph.D. degree from Xi’an University of Architecture and Technology, Xi’an, China, in 2017. Currently, she is an associate professor in School of Information and Control Engineering at Xi’an University of Architecture and Technology. Her research interests include digital image restoration and cultural relics big data analysis.
Zhan Wang received his master’s degree from University of Bologna, Italy, in 2008. Currently, he is an associate researcher in Technology Analysis and Testing Center at Shaanxi Provincial Institute of Cultural Relics Protection, Xi’an, China. His research interests include cultural relics detection analysis and researching.
Jialin Liu is a master student in School of Information and Control Engineering at Xi’an University of Architecture and Technology. His research interests include multispectral images processing.
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All authors take part in the discussion of the work described in this paper. The author KW wrote the first version of the paper. The author JL did part experiments of the paper. HW, MW, and ZW revised the paper in different versions of the paper, respectively. All authors read and approved the final manuscript.
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Wang, K., Wang, H., Wu, M. et al. RETRACTED ARTICLE: A method for spectral image registration based on feature maximum submatrix. J Image Video Proc. 2018, 140 (2018). https://doi.org/10.1186/s1364001803774
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DOI: https://doi.org/10.1186/s1364001803774