An Efficient Contrast Enhancement Method for Remote Sensing Images

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 14, NO. 10, OCTOBER 2017

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An Efficient Contrast Enhancement Method for Remote Sensing Images Jiahang Liu, Member, IEEE, Chenghu Zhou, Peng Chen, and Chaomeng Kang

Abstract— Remote sensing images often suffer low contrast. Although many contrast enhancement methods have been proposed in recent literature, the efficiency and robustness of remote sensing image contrast enhancement is still a challenge. In this letter, a novel self-adaptive histogram compacting transformbased contrast enhancement method for remote sensing images is presented to meet with the requirements of automation, robustness, and efficiency in applications. First, the histogram of an input image is optimized into compact and continuous status with the constraints of the merging cost, the moderate global brightness, and the entropy contribution of gray levels. Then, a local remapping algorithm is proposed to catch more details during the course of gray extending with the linear stretch. Finally, a dual-gamma transform is proposed to enhance the contrast in both bright and black areas. Experimental and comparison results demonstrate that the proposed method yields better results than the state-of-the-art methods and maintains robustness in different cases. It provides an effective approach for remote sensing image automatic contrast enhancement. Index Terms— Contrast enhancement (CE), histogram compacting transform (HCT), histogram modification, histogram transform, linear stretch (LS), image quality.

I. I NTRODUCTION EMOTELY sensed images have been widely applied in many fields, such as surveying and mapping, urban planning, disaster monitoring, geographical information, and so on. As a carrier of information, the image quality highly influences the interpretation of information. One of the most important quality factors comes from their contrast [1]–[3]. It is unfortunate that the narrow dynamic range of brightness and low contrast is a common problem of remotely sensed images. Thus, contrast enhancement (CE) plays an important role in visual quality improvement for remotely sensed image processing and other fields [4]. CE is typically used to adjust the global brightness and contrast of digital images [5]. The purpose of CE is to improve

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Manuscript received May 19, 2017; revised June 21, 2017; accepted July 8, 2017. Date of publication August 11, 2017; date of current version September 25, 2017. This work was supported in part by the National Key Research and Development Program under Grant 2016YFF0103604 and in part by the Western Young Scientist Program of the Chinese Academy of Sciences under Grant XAB2015A07. (Corresponding author: Jiahang Liu.) J. Liu is with the Laboratory of Remote Sensing and Intelligent Information System, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences (CAS), Xi’an 710119, China, and also with the Institute of Geographic Sciences and Natural Resource Research, CAS, Beijing 100101, China (e-mail: [email protected]). C. Zhou is with the Institute of Geographic Sciences and Natural Resource Research, Chinese Academy of Sciences, Beijing 100101, China. P. Chen is with the State Key Laboratory of Satellite Ocean Environment Dynamics, State Oceanic Administration, Hangzhou 310012, China. C. Kang is with the Laboratory of Remote Sensing and Intelligent Information System, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China. Digital Object Identifier 10.1109/LGRS.2017.2730247

the interpretability or obtain a more visually pleasing and informative image [6], [7]. Histogram equalization (HE) is one of the most popular categories of CE. It stretches the dynamic range of the image histogram and attempts to produce a uniform output histogram [8], [9]. However, HE often produces undesirable artifacts and dramatically changes the character of the image [10]. Besides these, it often makes some of the uniform regions become saturated with very bright or dark look. To avoid drawbacks of classic HE, several improved methods, such as regularized-histogram equalization and the discrete cosine transform (RHEDCT) [11], adaptive gamma correction with weighting distribution (AGCWD) [4], gradient and intensity histogram equalization (GIHE) [12], and so on, have been proposed in the latest literature. Although these methods can obtain more pleasing results than the classic HE, unnatural look and robustness are still problems. Also, brightness preserving does not mean a good result, as low brightness image is very common for remote sensing images. To make the mean brightness to a moderate level is more important than to preserve its original mean brightness. Then, some other types of CE have been proposed. In [14], a multiscale retinex [13]-based probabilistic method with simultaneous illumination and reflectance estimation algorithm is presented. Atta and Ghanbari [3] proposed a CE method using discrete cosine transform pyramid and singular value decomposition (DCTPSVD). However, these methods often suffer from halo artifacts or artificial noises in the results. Linear stretch (LS) is another important CE category and has the advantages of simplicity and fast execution performance. The LS has three types: min–max (MMLS), percentage (PtLS), and piecewise (PwLS) linear contrast stretches. It is known that the LS method highly depends on the stretch coefficient. If the range of pixel values is close to the full dynamic range ([0, 255] for 8-b image), the MMLS method will lose its efficacy. The PtLS method fails to process U, V, or their combined types, as its operation is executed only to the ends of a histogram. The PwLS method requires to manually determinate parameters for section determination and does not apply to automatic operation. Because of these drawbacks, LS methods usually cannot obtain a pleasurable result in practice though it has some important virtues. To mass remote sensing processing, automation, reliability, and applicability are necessary. Also, a good CE technique should not only provide uniform contrast of the entire image but also be convenient for implementation [15]. For this purpose, a novel CE method for remote sensing images is presented in this letter. It is expected to avoid the drawbacks of the existing methods and keep its efficiency and robustness in the general cases. Several experiments and comparisons with AGCWD [4] (2013), DCTPSVD [3] (2013),

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GIHE [12] (2015), and RHEDCT [11] (2015) were conducted to verify the performance of the proposed method in different cases. II. P ROPOSED I MAGE E NHANCEMENT M ETHOD A. Definition of Compact Histogram Two basic problems widely exist in remote sensing images. One is that image brightness often lies in a narrow range. Another is that few pixels often occupy a big range or a large number of pixels only contain a small number of gray levels. Obviously, this is unsuitable and inevitably leads to low contrast. Furthermore, small-probability levels removal will cause almost no loss of image entropy. If these smallprobability gray levels are removed, a redundant histogram will change into a compact and continuous histogram with a smaller distribution range. Then, a compact histogram can be defined: to a certain probability ζ , if the probability of every gray level between the minimum and maximum is not less than ζ , then the histogram is called a compact histogram with ζ measurement, compact histogram for short, and this property of the histogram is called the compactness with ζ probability. B. Histogram Compaction Transform For convenience, we use the parameters ζ , L E , L S , L T , L i , and Pi to denote the basic probability, the initial searching gray level, the starting gray level of current searching process, the current output gray level, the i th gray level, and its probability, respectively. Then, the algorithm of the proposed histogram compacting transform (HCT) can be described as follows. Step 1: Determine the basic probability ζ and the initial searching gray level L E , and initialize L S = L T = L E . Then, take L S as the starting position and search the histogram along the right direction. Step 2: Calculate the cumulative probability CP1 and CP2 until (1) is satisfied ⎧ L S +r ⎪ ⎪ ⎪ Pi = C P1 ≤ ζ ⎨ i=L S (1) L s +r+1 ⎪ ⎪ ⎪ Pi = C P2 > ζ. ⎩ i=L S

Step 3: calculate the merging cost i = C Pi − ζ , i = 1, 2. Step 4: If 1 ≤ 2 , then all the levels in [L S , L S + r ] are mapped to the output level L T and record this mapping relationship. Use CP1 as the probability of output level L T , and then jump to the step 6. Step 5: If 1 > 2 , then all the levels in [L S , L S + r + 1] are mapped to the output level L T and record this mapping relationship. Use CP2 as the probability of output level L T . Step 6: Update the starting searching level L S and output gray level L T to their next gray level, and make r = 0. Step 7: Repeat steps 2 through 6 until the entire right part of the histogram has been processed. Step 8: Process the left part of the histogram following the above-mentioned steps until the entire histogram has been processed. Accordingly, the intervals in steps 2, 4, and 5 should be replaced by [L S − r, L S ] or [L S − r − 1, L S ]. Step 9: According to the mapping relation, output the revalued histogram.

Fig. 1. Image histogram and its compacted results. (a) Original. (b) Using 2% clip. (c) Using HCT.

As the HCT aims to effectively compress the histogram distribution range of an image, the initial searching position can start anywhere in the histogram. Generally, the mean level is used as the initial searching position, and the ideal probability ζ0 is used as the basic probability. To an image quantized by N bits with a size of H × W pixels, ζ0 and L E can be defined by (2) ζ0 = 1 2 N LE =

N −1 2

pi L i .

(3)

i=0

In practice, a redundant histogram like Fig. 1(a) is general. In such cases, it is improper to use the same basic probability to compact every part of the histogram, and is better to use two or more basic probabilities. In the proposed method, the mean level is used to divide the histogram into two parts, and the parameters ζ L and ζ R are used to denote the basic probability for each part, respectively. They are estimated by ⎧ (L E − L Min ) ⎪ ⎨ζ L = 2ζ0 (L Max − L Min ) (4) (L Max − L E ) ⎪ ⎩ζ R = 2ζ0 (L Max − L Min ) . The important sense of (4) is not only as a basic reference, but also as a way to make the middle value close to the mean value after compacting. This means that it can locate the mean value in the middle part of the full dynamic range after linear extension and can lead to a moderate global brightness. Fig. 1 shows the behavior of our proposed HCT in histogram compaction. Fig. 1(a) is an original histogram with a range of [39, 255], and Fig. 1(b) is the histogram compressed through 2% clipping method at the both ends. Its range is [49, 251]. Only 15 levels are reduced, and its compression ratio (CR) is about 6.45%. Fig. 1(c) is the result by HCT and its range is [43, 115] with the CR of 66.36%. Their corresponding information entropy (IE) losses are about 1.7% and 6.8%, respectively. If the IE of the compressed result by HCT keeps to 5.9716, which is close to the result by 2% clipping (about 5.9763), then the CR by HCT is about 34.48%. It is clear that the proposed HCT method is more effective. C. Proposed Method The proposed method is mainly based on the HCT and the LS, and then it is called as HCTLS. This HCTLS is composed of three main steps. First, the HCT is used to compact the gray range of an image. Second, a local remapping method is proposed to maintain more details, which are integrated into LS for fast calculation. Generally, the compacted gray range is smaller than the range of full dynamic range. This means that there exist some unused levels between the two used neighboring levels when the compacted histogram is linearly

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TABLE I Q UANTITATIVE M EASUREMENT R ESULTS FOR THE C OMPARED M ETHODS

stretched into the full range of quantization. Suppose that the gray level k in the compacted histogram is mapped by the m levels in the original histogram. After LS, the level k is mapped to L a and its neighboring level k + 1 is mapped to L b . Furthermore, suppose that there are n (≥1) levels in [L a , L b ). This means that n−1 levels are not used. Thus, these m levels in the original histogram can be linearly remapped into [L a , L b ) rather than only to level La. We call this operation a Local Remapping. Through this operation, more details can be maintained. Finally, a dual-gamma function is proposed for improving the image details both in dark and bright areas. Let f (x, y) denote the original value at the point (x, y) with normalized status. Then, the output g(x, y), modified by the dual-gamma function, can be defined as g (x, y) = βα γ + α (1 − β γ ) (5) α = f (x, y), β = 1 − f (x, y). The purpose of the dual-gamma modification is to increase the visual appearance in dark and bright areas. The degree of gamma modification depends on the γ value. If the γ value is 1, then the curve of the dual-gamma function changes to a straight line. In our proposed method, we use the mean value L E to dynamically estimate the value of γ , which is defined by

L E 128, if L E ≤ 128

(6) γ = 1 − (L E − 128) 128, otherwise where 128 is the middle level for an 8-b quantized image and L E is defined by (3). It is noted from (6) that γ is no more than 1, and it changes with the mean value of an image. Besides this, 0.7 is set as the lower limit of γ in experience to avoid this operation highly affects the global appearance of the result. According to the principles of HCT and HCTLS explained earlier, their properties can be summarized as: 1) HCT can remove zero- and small-probability levels from a histogram, and it makes the histogram compact and continuous and 2) as HCT operates on every gray level, HCTLS can be applied to any type of histogram and maintain its applicability. III. E XPERIMENTS AND A SSESSMENTS A. Data Set Test image A [see Fig. 2(a)] is a CE-1 panchromatic image with 512 × 426 pixels, which was obtained in the lunar polar zone. Test image B [Fig. 3(a)] is a block of the GF-1 panchromatic image. Its original size was 18192 ×

Fig. 2. Test image A and its enhanced results using different algorithms. (a) Original image. (b)–(f) Enhanced results using DCTPSVD, AGCWD, GIHE, RHEDCT, and the proposed HCTLS method, respectively.

18 000 pixels. As this size is too large to show, an image block with 500 × 1000 pixels is cut out for displaying. Test image C [see Fig. 4(a)] is an 8-b gray image with a size of 512 × 512 pixels and was obtained from the University of Southern California (USC)–Signal and Image Processing Institute (SIPI) image database (http://sipi.usc.edu/database/?volume=textures). B. Assessment Method Evaluating the degree of image enhancement is not an easy task and there is no single objective criterion in the literature that gives consistently meaningful results for all images [3]. It is necessary to combine a subjective evaluation with a quantitative evaluation using numeric characteristics IE = −

N −1 2

pi log2 pi

(7)

i=0

MB =

N −1 2

i=0

pi L i

(8)

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Fig. 3. Test image B and its enhanced results using different algorithms. (a) Original image. (b)–(f) Enhanced results using DCTPSVD, AGCWD, GIHE, RHEDCT, and the proposed HCTLS method, respectively.

H −1 W −1 1

SD = [I (x, y) − M B]2 (9) WH x=0 y=0 H −2 W −2 ∂ I (x, y) 2 1 ∂ I (x, y) 2 + . MG = WH ∂x ∂y x=0 y=0

(10) The standard deviation (SD), the mean gradient (MG), the mean brightness (MB), and the IE are often used to indicate global contrast, local contrast, global brightness, and image entropy, respectively, and they are widely used for quantitative assessment. These four indexes will be used for the objective evaluation in this letter, and they are defined by (7)–(10), respectively. In general, a large SD indicates that the image has strong contrast, and a large MG indicates good definition. However, too high SD or MG is often accompanied by an unnatural look, and the quality of the image is decreased. Too bright or too low brightness also results in a bad visual perception. As the most important aim of image enhancement is to provide a better visual look, and since currently no quantitative assessment can effectively and accurately evaluate image quality, a subjective evaluation should be the primary approach in this letter. Namely, if the visual look of an image is poor, it will be determined to be poor regardless of the values of the other quantity indexes. C. Results and Assessment Fig. 2(b)–(f) shows the enhanced results of test image A by DCTPSVD, AGCWD, GIHE, RHEDCT, and the proposed

method, respectively. From a global perspective, most parts of the image are very dark, a few areas are very bright, and a few areas have medium brightness. After enhancement, the image contrast resulting from the different methods has been improved to some degree, but differences can be seen. The DCTPSVD results display some additional noise and visual qualities are degenerated in some areas. Although AGCWD enhanced the details in dark areas, many of the bright parts in the original image became too bright to observe in the enhanced result, and some details were lost. RHEDCT enhanced the contrast to some degree, but its effectiveness was limited. Comparing with the others, both GIHE and HCTLS not only enhanced the contrast, including the dark areas, but also achieved global brightness to the proper degree. Thus, the global results of GIHE and the proposed HCTLS are better than those of the others. Fig. 3(b)–(f) shows enhanced image results with comparing methods. From the original image [Fig. 3(a)], its histogram, and a section shown in the original resolution [Bottom of Fig. 3(a)], it can be seen that most of the values lie in a narrow range and the image has a low bright appearance. The DCTPSVD results show improved global brightness and contrast, but it is also clear that some of the details are blurred. For the AGCWD results, the image brightness was excessively increased, which led to a saturated and artificial appearance. The same effect occurred in the GIHE results. Different from the others, the results using RHEDCT and HCTLS are superior in both the global and local sense. Carefully comparing with Fig. 3(e) and (f), it can be seen that the HCTLS result is a little better at revealing local details than that of RHEDCT. Fig. 4(a) is obtained from USC-SIPI image database. It contains many subblocks, and every block has a different gray level (about a 15-level difference) with different textures. The image quality is very good and it can be used as an ideal tool for assessing the algorithms. With the GIHE result [Fig.4(d)], the brightness of the second block (marked 2 in the image) has been excessively reduced (to about 5, but it was 15 in the original) and the difference from block 1 was decreased (about 0); also, the texture of block 5 is degenerated. Furthermore, both the woman’s hat and face are greatly smoothed and it is difficult to distinguish them, and the continuous gray bar in the resulting image is excessively distorted. With the AGCWD result [Fig. 4(c)], the global brightness is greatly increased, which causes the brightness difference among subblocks 13 to 16 to decrease, even though these differences can be clearly distinguished in the original image. Similar to the results for test, the DCTPSVD result [Fig. 4(b)] for test inevitably contains noise. Fig. 4(e) and (f) shows the RHEDCT and HCTLS results, respectively, and it is clear that these two results are superior to the others both in global brightness and contrast, and the details of the woman’s hat and face are well maintained. Carefully observing the subblocks marked 14 and 15 in Fig. 4(e), it can be seen that some stripe noise was generated by RHEDCT, which caused a fluctuation of about six gray levels in these subblocks (14: 218 to 224; 15: 234 to 241). But in the original image, the pixels in these subblocks have the same gray value (about 219 and 236 for 14 and 15, respectively). This experiment indicates that RHEDCT will cause additional noise and generate artificial texture in smooth

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Regarding the computational time, our experimental program is designed with the C# language, and the runtime hardware is a 2.39-GHz CPU with 4.0-GB RAM. The time needed to process an image with the size of test image B (about 18000 × 18000 pixels) is about 11 s, and the time for small images approaches that of the classic LS. IV. C ONCLUSION This letter proposes a novel histogram compaction transformation-based self-adaptive image enhancement method for remotely sensed images. Various experiments were conducted to evaluate the behaviors of the proposed method in different cases. The experimental results indicate that the proposed method not only can effectively enhance the image contrast and provide a better visualization of the results than the state-of-the-art methods but it also maintains its robustness in different cases. Thus, it is a practical approach for remote sensing image enhancement. R EFERENCES

Fig. 4. Test image C and its enhanced results using different algorithms. (a) Original image. (b)–(f) Enhanced results using DCTPSVD, AGCWD, GIHE, RHEDCT, and the proposed HCTLS method, respectively.

areas. However, with the result from our proposed HCTLS, the textures are well maintained and smooth areas in the original image retain their smoothness in the enhanced result. This indicates that the proposed HCTLS not only is superior with textures and structures, but it also does not cause additional noise. The experimental results clearly indicate that the proposed HCTLS method can effectively improve image contrast and global image quality. Other examined methods can provide a good result in some cases, but not in others. The HCTLS maintains its effectiveness and robustness across different cases. Quantity assessments based on (7)–(10) are shown in Table I. For the reason mentioned above, the digital assessment is only a complementary description. It should be noted that the image entropies of the DCTPSVD and RHEDCT results are higher than those of the original images. The reason is that some operations in the inverse discrete cosine transform (DCT) have filtered the high frequencies and caused additional smoothness. This of course is subject to interpretation. Because of this, the enhanced results by DCTbased methods have more gray levels than the original image, and this leads to the high artificial image entropy. This property can also be observed visually. Thus, the image entropies from these methods were not considered in the comparison. For the AGCWD, GIHE, and HCTLS results, the entropies are decreased but normal. The subjective assessment indicates that our proposed HCTLS can maintain the entropy of the original image when it effectively improves image contrast.

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