Experimental Tests of Image Fusion for Night Vision ∗ Yin Chen ECE Department Lehigh University Bethlehem, PA 18015, USA
[email protected]
Abstract – Image fusion techniques have begun to play a very important role in night vision systems. In recent years, various image fusion algorithms have been developed to perform the task. However very few comprehensive studies have been conducted to evaluate the performance of fusion methods for night vision applications. In this paper we focus on fusion algorithms especially for the night vision application and employ experimental testing to compare their performance. To judge the performance of image fusion algorithms, we investigate both subjective and objective evaluation measures. Human evaluations of the fusion results are presented. Furthermore, to evaluate image fusion algorithms objectively, we studied various image quality measures, which include some standard quality metrics and other newly developed methods. Extensive performance evaluation experiments show that observers generally prefer the SiDWT and Laplacian pyramid fusion scheme for the considered test images. Further the objective quality measure results show that edge based quality metrics follows human evaluations much closer than the other methods in most of the cases we considered.
Rick S. Blum ECE Department Lehigh University Bethlehem, PA 18015, USA
[email protected]
the procedure of image fusion can enable one to perceive features that are impossible to perceive with any individual type of sensor, thus improving human visual perception and automatic computer analysis. Although image fusion approaches and their applications have been widely investigated [1, 2, 3, 4, 5, 6], few studies have been conducted to evaluate these fusion methods for the night vision application. Since it is known that image fusion algorithm performance is application dependent, we focus on fusion algorithms especially for night vision applications and compare their performance. In Section 2, we address a general image fusion framework and describe the algorithms selected for study. In Section 3, we subjectively compare the performance of the selected fusion algorithms for night vision applications. In Section 4, objective quality measures for image fusion are investigated and the results show that some quality metrics disagree with human evaluation in a substantial majority of the cases tested. Finally conclusions are discussed in Section 5.
Keywords: Image fusion, image quality, night vision.
1 Introduction Sensors can greatly enhance our ability to monitor our surroundings. In many situations, a single sensor is not sufficient to provide an accurate perception of the real world. Thus multisensor data fusion has become an intense research area in recent years. Multisensor fusion is a process of combining data from different sensors into a single representational format. The basic objective of data fusion is to derive more information through combining, than is present in any individual input sensor. Here we focus on fusing images or videos. The source images (or videos) will usually be collected from different types of sensors, for example, visual cameras, lowlight night vision cameras, infrared cameras (IR), millimeter wave (MMW) cameras, and X-ray imagers. The images generated from these sensors have different characteristics, providing different and complementary information. Thus ∗
This material is based on work supported by the U. S. Army Research Office under grant number DAAD19-00-1-0431. The content of the information does not necessarily reflect the position or the policy of the federal government, and no official endorsement should be inferred.
2
Image Fusion Algorithms
The theory of image fusion has advanced rapidly in the past few years. Image fusion approaches ranging from extreme simplicity to considerable complexity have been proposed. Recently, a generic framework covering a large set of these approaches has been given in [7]. Focusing on cases where no training data are available, we considered many of the different grayscale image fusion algorithms described in the framework from [7], including those using several different multiscale transforms and those using no transforms (for example additive (ADD) fusion which weights the two source images directly [3]). It is worth noting that our tests included the most frequently employed multiscale decomposition-based (MDB) fusion approaches: the Laplacian (LAP) pyramid algorithm [8], the filter-subtract-decimate (FSD) pyramid algorithm [9], the gradient (GRAD) pyramid algorithm [10], the morphological (MORPH) pyramid algorithm [11], the contrast pyramid (CONTR) algorithm [1], the ratio of low pass pyramid (RoLP) algorithm [12], the discrete wavelet transform (DWT) algorithm [13], and the shift invariant DWT (SiDWT) fusion algorithm [14]. Many other algorithms were also considered. For each basic algorithm configu-
(a) CONTR Pyramid
(b) RoLP Pyramid
Fig. 1: Fused examples of CONTR (a) and RoLP (b). ration, multiple alternatives for the activity level measurements, grouping methods, combining methods, and consistency verification methods from the framework described in [7] were considered. These algorithms were tested for fusing low-light images produced by an image-intensified TV (IITV) sensor with infrared images produced by a Forward Looking Infrared (FLIR) camera, after registering these images as well as possible. Based upon the preliminary testing, we determined the most promising fusion approaches to be those given in Table 1. It is noted that the alternative of the combining method listed in Table 1 is employed at the lowest frequency band for the pyramid transform-based fusion, and low-low band for DWT and SiDWT fusion. The combining method for other frequency bands is “choosing maximum” in our tests. In the process of determining the methods in Table 1, we eliminated many algorithms. Some of them performed very poorly. For example, consider the CONTR pyramid and the RoLP pyramid fusion methods. As shown in Figure 1, there are too many algorithm-created spots in the fused images. Although there are alternative grouping and consistency verification methods that somewhat improve on the fused images shown in Figure 1, we did not obtain significantly better results. Therefore, we eliminated the CONTR fusion and the RoLP fusion algorithm from our subjective testing list. Many other methods were eliminated for similar reasons (poor performance in a reasonable number of cases) or because they always performed similarly to other approaches.
3 Experimental Tests There are 28 sets of night vision images being examined. We show one example of source images and all the corresponding fused images in Figure 2. The scene consists of a person walking in high grass, a dirt path behind the person, a cinder-block building in the background, and what appears to be some visible light in the scene to the right of the building and also above the building. This light is in addition to that visible above the trees. It is noted that the IR image shows clearly the person, the building and the road. On the other hand, the IITV image depicts the light visible on and above the building. Generally speaking, all the fused images provide improved situational awareness over either source image alone, because they show all the important scene features in one single image. From the judgement of a small evaluation group, the SiDWT2 and LAP2 fusion methods outperform the other methods, as shown in Figure 2 (o) and (k). They clearly
show the person, the building, the light, the road and the texture of the grass, trees and bushes. The alternative of either the “choosing maximum” or the “weighted average” combining method at the lowest or low-low frequency band generates different MDB fusion results with respect to contrast and brightness. In the case we show in Figure 2, it appears that images with the “choosing maximum” rule have overall better performance over those with the “weighted average” combining rule in this regard. In some other cases, the conclusion may be opposite. This is reasonable because the “choosing maximum” procedure tends to collect the highest intensity pixels for inclusion in the fused image [3]. In most cases, the FLIR image looks brighter than the IITV image so that the “choosing maximum” rule tends to select the high intensity pixels from the FLIR image. When the source FLIR image has overall low contrast, this could result in a lower contrast fused result. In some situations the IITV source images may contain what is reasonable to classify as noise. In such cases the “weighted average” combining method may smooth the noise. We note that, while the SiDWT and LAP fusion schemes consistently perform well, the choice of the best combining method is somewhat image and observer dependent based on our evaluations. The DWT2 fusion result is inferior to SiDWT2 and LAP2 due to the more noticeable artifacts at the sky/tree boundary as shown in Figure 2 (j). The FSD2 and GRAD2 fusion results look similar to each other, but they have relatively lower contrast than other “choosing maximum” MDB methods and seem blurred. As shown in Figure 2 (l) and (m), the texture of the trees, grass, bushes, and road is less perceptible. In Figure 2 (n), the MORPH2 pyramid fusion result shows good contrast and all important scene features. However the blocking effects in the area of sky/tree line interface make this method less attractive. The artifacts are probably caused by reversed contrast in the source images. All the MDB fusion methods tend to have better contrast when compared to the ADD fusion methods, as shown in Figure 2 (c). However we also notice that when using the “weighted average” combining method in an MDB fusion procedure, the MDB fused images may not have significant advantage over the ADD fusion method for the cases studied. For instance, when compared to the ADD fused image in Figure 2 (c), the DWT1 fusion result in Figure 2 (d) has a dark area around the person instead of a smooth background. This artifact is probably generated by the significant differences between the source images in this area and by the shift variance of the DWT method which could enlarge a small spatial misalignment between the IITV and FLIR images. Figure 2 (e) shows that the fused images employing the LAP1 pyramid have similar artifacts as those in the fused images employing the DWT1 with the “weighted average” combining method. On the other hand, the SiDWT1 approaches provide some improvement which is probably due to its shift invariance property. The FSD1 and GRAD1 pyramid fusion methods (Figure 2 (f) and (g)) again perform similarly when we employ the “weighted average” combining rule. However, they do not show the texture of trees and roads very clearly. The MORPH1 fused image in Figure 2 (h) has a better contrast than other MDB
Fusion Scheme
Reference
MSD level
Activity Measure
Grouping
Combining
Weights(IITV/IR)
Verification
ADD
[3]
None
None
None
None
70%/30%
None
DWT1
[13, 7]
4
Coefficient based
None
Weighted Average
70%/30%
None
LAP1
[8, 7]
4
Coefficient based
None
Weighted Average
70%/30%
None
FSD1
[9, 7]
4
Coefficient based
None
Weighted Average
70%/30%
None
GRAD1
[10, 7]
4
Coefficient based
None
Weighted Average
70%/30%
None
MORPH1
[11, 7]
4
Coefficient based
None
Weighted Average
70%/30%
None
SiDWT1
[14, 7]
4
Coefficient based
None
Weighted Average
70%/30%
None
DWT2
[13, 7]
4
Coefficient based
None
Choosing Maximum
None
None
LAP2
[8, 7]
4
Coefficient based
None
Choosing Maximum
None
None
FSD2
[9, 7]
4
Coefficient based
None
Choosing Maximum
None
None
GRAD2
[10, 7]
4
Coefficient based
None
Choosing Maximum
None
None
MORPH2
[11, 7]
4
Coefficient based
None
Choosing Maximum
None
None
SiDWT2
[14, 7]
4
Coefficient based
None
Choosing Maximum
None
None
Table 1: Fusion schemes (and their specifications) to be evaluated
fusion schemes using the “weighted average” combining methods, but there are too many artifacts at the sky/tree boundary and on the tree trunks. We applied similar testing to all 28 groups of images with different scenes. The observer’s feedback indicate that even though there are exceptions for some images, the SiDWT and LAP fusion methods generally outperform the others and this is true when either the “choosing maximum” rule or the “weighted average” rule is used. The DWT, FSD and GRAD pyramid fusion methods are the next best followed by MORPH and ADD fusion methods. The trend is followed in most cases tested.
4 Objective Quality Measures Designing objective image fusion metrics for cases without an ideal or reference image (that describes what the perfect fusion scheme would produce) is a very difficult task but such metrics are highly desired. For the night vision application, the ideal composite image is normally unknown. Among the limited number of methods that have been proposed in the literature for image fusion quality assessment without an ideal image, most of them are not very suitable for the night vision application [15, 16]. Some researchers assess the fused images by using subjective tests [17, 18]. However, although subjective tests can sometimes be accurate if performed correctly, they are inconvenient, expensive, and time consuming. Further, it is impossible to use them to continually adjust system parameters in a real time manner. Hence, an objective performance measure that can accurately predict human perception for night vision would be a valuable complementary method. Only a few objective image fusion performance evaluation measures which do not require the availability of an ideal image have been put forward in the literature for multi-sensor image fusion and we review these approaches in the rest of this section. Generally speaking, there are two types of image fusion quality measures. One type employs some standard quality metrics such as standard deviation, entropy, and SNR estimation [19] to extract features from the fused image itself. The other type utilizes features of both the fused image and source images. The second type has been considered us-
ing as cross entropy measures, information based measures [20], objective edge based measures [21, 22], and universal index based measures [23].
4.1
Standard Deviation (SD)
For a fused image of size N ×M , its standard deviation can be estimated by M N 1 (Cf (i, j) − m) ¯ 2 SD = N M i=1 j=1 where C(i, j) is the (i, j)th pixel intensity value and m ¯ is the sample mean of all pixel values of the image. We know that SD is composed of two parts, the signal part and the noise part. This measurement will be more efficient in the absence of noise, when it represents the signal strength only.
4.2
Entropy (EN)
Entropy has often been used to measure the information content of an image [24]. Entropy has also been employed to evaluate the performance of image fusion [25]. Using entropy, the information content of an image is EN = −
G
p(i) log2 {p(i)}
i=0
where G is the number of gray levels in the image’s histogram (which can be 255 for a typical 8-bit image) and p(i) is the normalized frequency of occurrence of each gray level, i.e., the histogram of the image. To sum up the selfinformation of each gray level from the image, the average information content (entropy) is estimated in the units of bits per pixel. It should be noted that entropy is also sensitive to noise and other unwanted rapid fluctuations.
4.3
SNR Estimation (QS)
In [19], Zhang and Blum have described a method to estimate the quality of noisy images, which is based on the
(a) IITV image
(b) FLIR image
(c) ADD
(d) DWT1
(e) LAP1
(f) FSD1
(g) GRAD 1
(h) MORPH1
(i) SiDWT1
(j) DWT2
(k) LAP2
(l) FSD2
(m) GRAD2
(n) MORPH2
(o) SiDWT2
Fig. 2: Source images and the fused results.
edge intensity image histograms. A mixture model is used in conjunction with the EM algorithm to model the edge intensity image obtained by using edge detection on the image to be evaluated. The histogram of edge intensity image ∇I is modeled as a mixture of Rayleigh probability density functions as f (r) =
M
2
ωi
i=1
− r2 r exp 2σi . 2 σi
A method to directly estimate the signal-to-noise ratio (SNR), of an image is based on summing over histogram bins to approximate ∞ QS = f∇I (r)dr 2µ
where µ is the mean of ∇I. It has been proven that the value of QS for a more noisy image is always smaller than the value of QS for an image with less noise. In addition, other degradation such as blurring can be estimated with this model [19].
4.4 Cross Entropy (CE) The overall cross entropy of the source images A, B and fused image F , is defined as [26] (pA is p for image A) CE(A, F ) + CE(B, F ) CE = 2 where CE(A, F )(CE(B, F )) is the cross entropy of the source image A(B) and fused image F CE(A, F ) =
G
pA (i) log2
i=0
pA (i) pF (i)
G
pB (i) pB (i) log2 CE(B, F ) = . pF (i) i=0
4.5
Information Based Measure (MI)
Mutual information has been employed as a means of assessing image fusion quality. Define the joint histogram of source image A(B) and the fused image F as pF A (f, a)(pF B (f, b)) . Then the mutual information between source image and the fused image is [20] IF A (f, a) =
f,a
IF B (f, b) =
f,b
pF A (f, a) pF A (f, a) log2 . pF (f )pA (a) pF B (f, b) log2
pF B (f, b) . pF (f )pB (b)
Image fusion performance is measured by the size of M IFAB = IF A (f, a) + IF B (f, b). where a larger measure implies better image quality.
4.6
Objective Edge Based Measure (QE)
Xydeas and Petrovic [21, 22] addressed an objective fusion performance measure associated with edge intensity and orientation. The measure is obtained by evaluating the amount of edge information that is transferred from source images to the fused image. A Sobel edge operator is applied to yield edge strength g(i, j) and orientation α(i, h) ∈ [0, π] for each pixel of the image. Then the relative strength and orientation values, GAF (i, j) and ΦAF (i, j), of input image A with respect to fused image F are defined as gF (i, j) gA (i, j) AF G (i, j) = gA (i, j) gF (i, j)
if gF (i, j) > gA (i, j) otherwise
and ΦAF (i, j) = 1 −
|αA (i, j) − αF (i, j)| . π/2
The edge preservation values QAF from input image A to fused result F is formed by the product of a sigmoid mapping function of the relative strength and orientation factors. Some constants κ, σ, and Γ determine the shape of the sigmoid mapping as QAF (i, j) = (1 + expκg (G
Γg Γa . )(1 + expκα (ΦAF (i,j)−σα ) )
AF (i,j)−σ ) g
In our test, κg = −15, σg = 0.5, Γg = 1.0006 and κα = −22, σα = 0.8, Γα = 1.0123 have been employed. The AB/F overall objective quality measure QEp is obtained by weighting the normalized edge preservation values of both input images as QEpAB/F =
N M i=1
AF (i, j)ω A (i, j) + QBF (i, j)ω B (i, j) j=1 Q .
N M A B i=1 j=1 (ω (i, j) + ω (i, j))
In general the weights ω A (i, j) and ω B (i, j) are a function of edge strength. The range of QE is between 0 and 1, while 0 indicates the complete loss of source information, and 1 means the “ideal fusion” without loss of source information [22].
4.7
Universal Index Based Measure (UI)
Piella and Heijmans [23] proposed a new quality metric for image fusion based on research by Wang and Bovik [27] on a structural similarity (SSIM) measure. Given two discrete-time non-negative signals x = (x1 , . . . , xn ) and y = (y1 , . . . , yn ), we let µx , σx2 and σxy be the mean of x, the variance of x, and the covariance of x and y, respectively. Then the structural similarity index between signal x and y is defined as SSIM =
2µx µy σxy 2σx σy · · . σx σy µ2x + µ2y σx2 + σy2
Fusions Additive DWT1 LAP1 FSD1 GRAD1 MORPH1 SiDWT1 CONTR1 RoLP1 DWT2 LAP2 FSD2 GRAD2 MORPH2 SiDWT2 CONTR2 RoLP2
SD 36.4290 38.9434 40.2345 36.4459 36.4199 42.3541 39.0756 45.1546 43.7580 47.0168 47.6479 43.8442 43.8315 49.0287 47.3851 58.1269 51.1613
EN 6.6368 6.8680 6.8927 6.7232 6.7206 6.9710 6.8329 6.2915 6.9225 7.1646 7.1451 7.1106 7.1088 7.2369 7.1109 6.4099 6.6568
QS 15.4075 14.5335 14.8446 14.2176 14.2362 15.3862 14.7015 16.8178 16.9766 14.1588 14.4417 13.3304 13.3601 15.1978 14.2546 17.8570 17.5098
CE 1.6008 1.6482 1.6015 1.6763 1.6657 1.3877 1.6614 1.2113 1.4984 1.5010 1.2690 1.4394 1.4377 1.3227 1.3090 1.1822 1.6077
MI 0.9626 0.5813 0.6007 0.6610 0.6665 0.5703 0.6203 0.5876 0.5695 0.8292 0.8748 0.7420 0.7454 0.8711 0.8930 0.4952 0.5938
QE 0.5123 0.5981 0.6287 0.5921 0.5989 0.5653 0.6394 0.4731 0.4014 0.6099 0.6417 0.6081 0.6146 0.5943 0.6505 0.3783 0.3880
UI 0.7532 0.7525 0.7691 0.7675 0.7705 0.7096 0.7717 0.6276 0.6405 0.7729 0.7919 0.7816 0.7847 0.7538 0.7954 0.5259 0.5831
Table 2: Results of Quality Measures for Various Fusion Schemes. The new quality index based on the SSIM measure gives an indication of how much of the salient information contained in each of the input images has been transferred into the fused image. First we calculate SSIM(a, f |w) and SSIM(b, f |w) which are the structural similarity measures between the input images and the fused image in a local window w. Then a normalized local weight λ(w) is obtained to indicate the relative importance of the source images. The fusion quality index is calculated by UI =
1 (λ(w)SSIM(a, f |w) |W | w∈W
+ (1 − λ(w))SSIM(b, f |w)) where W is the family of all windows and |W | is the cardinality of W . We evaluate the above-mentioned quality measures for several different fused night vision images. To compare the objective measures with human perceptual evaluation, we bring two poor night vision fusion schemes (CONTR and RoLP) back into consideration. Table 2 shows the average measures over 28 image sets, and the bold numbers indicate the top 3 largest values. We notice that the QE quality metric matches human evaluations more closely than do the other measures. Usually observers prefer the SiDWT and LAP fusion methods and the QE measure indicates highest values for these two fusion schemes. For poor fusion approaches like the CONTR and RoLP pyramids, QE assigns them much lower values, matching human evaluations. For the other fusion schemes like the DWT, FSD, GRAD, MORPH and ADD method, QE shows a trend which is close to the evaluation ratings from the observers. MI and UI measures match the observers evaluation trends in some cases but also give scores that differ in some other cases. For instance, MI produces the highest score for the ADD fusion method, contrary to human observers. It also gives a high value for MORPH2 even though there are many artifacts in the morphological fusion results which causes them to be ranked much lower
by human evaluators. The UI measure shows that FSD and GRAD are comparable to LAP and SiDWT, but usually the scene in the FSD and GRAD pyramid fusion images have lower contrast. Based on our test, it seems that other quality measures like SD, EN, QS, and CE show no apparent correlation to human perceptual evaluations. As we know, SD can provide some contrast information. From Table 2, MORPH2, CONTR2, and RoLP2 are the best with respect to SD. However looking at the images we know that the increased contrast level could result from the artifacts and algorithmcreated spots, which observers are not pleased with. EN is used to measure global information content in the fused image. The EN metric produces comparable quality values for the CONTR1 and RoLP1 pyramid fusion methods. This may be caused by the sensitivity of the EN metric to noise and other dramatic fluctuations in the image. Recall that entropy can not discriminate between useful information and noise. CE measures the cross information between the fused image and the source images. It faces the same problems as EN to distinguish the useful contents from noise. The SNR quality measure (QS) has been proved to estimate Gaussian noise very well in [19]. However, the results in Table 2 indicate that the noise in night vision images may not be modeled using the simple Gaussian model. The above comments are based upon the average values over 28 different images. It is noted that there are exceptions for some individual images. For instance, although QE seems the best measure, correlating to the human perceptual evaluation, there exist some cases where QE indicates a very high value for the CONTR fusion method, even higher than SiDWT and LAP fusion. However looking through those fused images, we find that the CONTR fusion results are not that good. For these cases, the majority of information in the fused images are transferred from one source image, for instance from IITV image, but little information is transferred from the FLIR image. Based on the definition of the QE metric, the edge preservation values indicate the information transferred from source im-
age to fused image and the overall QE metric is obtained by weighting the edge preservation values of both images. We believe that the exact constants and weights used in this method could be designed adaptively to make the QE metric more robust. On the other hand, we understand that one single quality metric may not work well in all cases. New quality measure study and sophisticated combination of current quality measures should be a topic of future study.
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5 Conclusions
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In this paper, we conducted subjective and objective performance evaluations for various fusion schemes applied in night vision applications. The experimental results show that human observers usually prefer the SiDWT and LAP fusion approaches for most of the tested images. The tests of objective quality measures show that the edge based metric (QE) better matches human evaluations than do the other methods.
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