I.J. Image, Graphics and Signal Processing, 2018, 2, 60-67
Published Online February 2018 in MECS (s-press/)
修改病句练习及答案>日语翻译发音DOI: 10.5815/ijigsp.2018.02.07
A Low-Complexity Algorithm for Contrast单身英文
Enhancement of Digital Images
Zohair Al-Ameen
Department of Computer Science, College of Computer Science and Mathematics,
University of Mosul, Nineveh, Iraq
Email:
Zaman Awni Hasan
Department of General Education, College of Education and Languages,
Lebane French University, Erbil, Kurdistan Region, Iraq
Email: zaman.iphone@mail.ru
Received: 31 October 2017; Accepted: 10 January 2018; Published: 08 February 2018
Abstract—As known, the contrast is a highly important feature by which the visual quality of digital images can be judged as adequate or poor. Hence, many methods exist for contrast enhancement, where the complexity of tho methods usually varies due to the utilization of different concepts. In this article, a simple yet efficient algorithm is introduced for contrast enhancement of digital images. The propod algorithm consists of four distinct stages: In the first stage, the hyperbolic sine function is applied to provide a simple contrast modification. In the cond stage, a modified power-law function is utilized to control the amount of contrast adjustment. In the third stage, the standard sigmoid function is ud to remap the image pixels into an “S” shape, which can provide further contrast enhancement. In the final stage, a contrast stretching function is applied to remap the image
pixels into their natural dynamic range. The performed computer experiments on different low-contrast images demonstrated the efficiency of the propod algorithm in processing synthetic and real degraded images, as it provided better and clearer results when compared to veral existing contrast enhancement algorithms. To end with, the propod algorithm can be ud as a contrast processing step in many image-related applications.
Index Terms—Contrast enhancement, Image processing, Low-complexity algorithm, Low-contrast.
I.I NTRODUCTION
Over the last decades, substantial progress has been made in the fields of digital image processing and computer vision by both professionals and rearchers [23]. Contrast enhancement is an important image processing field that plays an esntial role in improving the visible quality for a variety of image-related applications [4]. Moreover, it is considered an important processing step in different scientific applications [7]. The insufficient contrast in digital images can occur due to many reasons, including the deficient experti of the operator and the u of an inefficient device for image acquisition [1]. Commonly, the contrast is generated due to variance in luminance between two adjacent surfaces. Accordingly, the contrast of a given image can be determined via the intensity
difference of an object with other objects. Thus, if the image intensities have a restricted distribution within a limited range, the contrast of the obrved image would become low and its details would be obscured.
The low-contrast effect reduces the visual quality of an image and thus, it should be handled properly to provide acceptable quality for digital images [9]. Hence, it is desired to redistribute the intensities of a given image to the entire dynamic range in order to improve its contrast and provide an adequate reprentation for its information [10]. Generally, the methods for contrast enhancement of can be categorized into direct [2, 3] and indirect [5, 6] methods. In the direct methods, a certain contrast term is ud to define the image contrast. Since a digital image contains simple and complex patterns, using such contrast terms may fail to measure the contrast in a variety of images [31]. On the contrary, indirect methods try to improve the image contrast by redistributing the probability density. This means that the intensities of a given image can be reallocated within the natural range without using a certain contrast term [1].
Most of the methods are implemented either in the spatial domain or the frequency domain. Accordingly, the image is procesd as it is in the spatial domain, whereas in the frequency domain, the image is first transformed to its frequency version, then the processing occurs; after that, an inve
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r transformation is applied to view the image in the spatial domain [20]. Histogram-bad methods for contrast enhancement are the most famous indirect methods due to straightforward implementation [8]. Despite the major advantages of the indirect methods, many of such tend to produce undesirable degradations (e.g. under-saturation, over-saturation) to the procesd images [21]. Hence, a low-complexity
algorithm is introduced in this article to process low-contrast images rapidly and efficiently without introducing any undesirable degradations. The propod algorithm is developed experimentally and consists of 4 distinct steps, for which each step provides a positive contribution to the enhancement process. In addition, the enhancement ability of the propod algorithm is controlled using a single parameter. The developed algorithm is evaluated by a datat of synthetic and real degraded low-contrast images collected from various internet websites. The synthetic degraded images are ud for comparison purpos, while the real degraded images are ud for experimental purpos [32]. Moreover, the quality of the obtained results is measured using two reliable image quality asssment (IQA) metrics. Likewi, the propod algorithm is compared with three well-known contrast enhancement methods. To end with, most of the available contrast enhancement methods employ the concept of histogram equalization. In addition, most of the available contrast enhanceme
nt methods that provide acceptable results have a complex structure with a high number of calculations. However, the propod algorithm has a simple structure and does not utilize the concept of histogram equalization. To test the processing ability of the propod algorithm, various computer experiments on different low-contrast images are performed. The rest of the article is ordered as follows: in Section II, an abridged review of literature is delivered. In Section III, the propod algorithm is explained in detail. In Section IV, the results and their related discussions are provided. Finally, a conci conclusion is given in Section V.
II.R ELATED W ORKS
In this ction, various contrast enhancement methods are addresd to highlight some of the important concepts that have been utilized previously. In view of that, the dynamic histogram equalization [24] is an enhancement technique that works by partitioning the histogram of the input image depending on local minima and allocates certain gray-level ranges for every partition before processing them independently. The aforementioned partitions undergo further tests to avoid having dominating partitions. Another method of interest is fud logarithmic transform [25], as its enhancement is achieved by fusing the degraded image and its logarithmic counterparts.
In addition, the intensity surface stretching technique [26] process an image by stretching its intensity surface to the maximum range with consideration to the distances between the pristine intensity surface as well as the upper and lower intensity surfaces, which are created automatically from the pristine intensity surface by calculating the gamma transform and Gaussian smoothing. Moreover, the algorithm propod in [27] process an image using the theory of partitioned iterated function system (PIFS). The PIFS is utilized to generate a low-pass counterpart of the input image. The enhanced image is generated by adding the difference of the input image and its low-pass counterpart to the input image itlf. Furthermore, the Gaussian mixture modeling bad algorithm [28] process an image by modeling its gray-level distribution to generate gray-level intervals. The resulting image is created by changing the gray-level of pixels in each determined interval to the suitable output gray-level interval in relation to the cumulative distribution function and the dominant Gaussian element of the input interval.
Another interesting method is the spatial entropy-bad enhancement [29], as it distributes the spatial positions of gray-levels of an image rather than gray-level distribution. The spatial distribution is calculated for each gray-level by a histogram of spatial positions for all pixels with the same gray-level. From the spatial distributions of image gray-levels, the entropy measures are computed to gen
erate a distribution function, which is also mapped to another distribution function to attain the final enhancement. To end with, the method introduced in [30] works by iteratively solving a nonlinear diffusion equation. Through the process of diffusion, surround suppression is included in the conductance function to improve the diffusive strength in certain areas of the image. As en from the above-reviewed methods, various low and high intricacy concepts have been utilized for the purpo of contrast enhancement. Thus, developing a new algorithm that utilizes a simple concept and achieves acceptable enhancement is desirable in many computer vision and image processing applications.
III.P ROPOSED A LGORITHM
简历封面wordFig.1. The framework of the propod algorithm.
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The propod algorithm is developed experimentally with intention of providing an adequate enhancement for images with poor contrast. The framework of the propod algorithm is illustrated as in Fig. 1.
The propod algorithm starts by applying the hyperbolic sine function as an initial processing stage to provide a simple contrast modification. This function can be described as follows [11]:
2
x x
e e s --=
(1)
where, (x ) is the input low-contrast image, (s ) is the resulting image from the hyperbolic sine function. Then, the result of the previous stage is procesd by a modified version of the famous power-law function. The standard power-law function can be written as follows [12]:
p c s λ=*
(2)
where, (c ) is a scalar that controls the brightness of the power-law function, (λ) is a scalar that controls the enhancement of contrast. The values of the scalars must fulfill (c , λ > 0), where a higher (c ) value leads to a brighter result and a higher (λ) value leads to a less bright and contrast improved result. Thus, the above standard function is modified by removing the scalar (c ) becau it can lead to an undesirable increa in the brightness of the procesd image. Hence, the modified power-law function is written as follows:
y s λ=
(3)
where, (y ) is the resulting image from the modified power-law function. Afterwards, the standard sigmoid function is utilized to remap the image pixels in an “S” shape, which can provide further contrast enhancement. The ud sigmoid function can be written as follows [13]:
1
1y
w e -=
+ (4)
where, (w ) is the resulting image from the standard sigmoid function. As a final stage, a contrast stretching function is applied to remap the image pixels into their natural dynamic range [22]. The ud stretching function can be described as follows [14]:
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()()()
min max min w w f w w -=
-
savor(5)
where, min and max are the minimum and maximum pixel values, respectively. ( f ) is the final result of the propod algorithm. Finally, the benefits of this algorithm are fast processing speed, low computation cost, and artifact-free images with acceptable quality results.
IV. R ESULTS AND D ISCUSSION
In this ction, the esntial computer experiments and their related discussions are provided to validate the processing ability of the propod algorithm. To perform
proper experiments, real-degraded images were ud for experimental purpos, and synthetic-degraded images were ud for comparison purpos. The datat of this study was collected from different imaging databas across the internet. Regarding the comparable methods, veral contemporary and reliable methods were chon, in which the propod algorithm was compared with three of such methods namely, successive mean quantization transform (SMQT) [15], gradient distribution specification (GDS) [16], and exposure bad sub image histogram equalization (ESIHE) [17].
Regarding the ud IQA metrics, two well-known full-reference IQA metrics of universal image quality index (UIQI) [18] and structural similarity index (SSIM) [19] were ud to measure the quality of the results of comparisons. The results of the metrics are constant numbers, in which values near 1 indicate high-quality results and values near 0 indicate low-quality results. The experimental results obtained from processing various real-degraded grayscale images are shown in Fig. 2 – Fig.
4. The results of the performed comparisons can be en in Fig. 5 and Fig. 6. The recorded accuracy by the UIQI and SSIM metrics are provided in Table 1, Fig. 7 and Fig. 8. From the obtained experimental results in Fig. 2 – Fig. 4, it can be en that the propod algorithm provided visually pleasing results. Moreover, the recovered images show natural brightness, acceptable contrast with no visible flaws. However, the value of λ dif fers from one image to another due to the noticeable differences in the nature of the ud images and the variance in contrast levels for each procesd image.
From the obtain comparison results in Fig. 5 – Fig. 8 and Table 1, it is evident that the propod algorithm performed the best in terms of recorded accuracy and perceived quality, as it recorded the best UIQI and SSIM scores, as well as, it provided clear and unblemished results. This is significant becau the propod algorithm has a simple structure with a non-iterative nature and utilizes few calculations to achieve its task. Regarding the SMQT method, it provided very good performances especially when the contrast of the procesd image was verely reduced. However, its high computation and iterative natures made it slow in producing the desired results. Moreover, this method tends to increa the darkness of dim areas in the procesd images. Such limitations can lead to unclear results, especially for images with many dim areas.
Regarding the GDS method, it provided moderate contrast enhancement, yet the low-contrast effect is still visible in the procesd images. In addition, this method is somewhat slow due to its high computations utilization. Regarding the ESIHE method, it provided good performance when the procesd image had minor contrast reduction. However, it provided poor performance when the procesd image had major contrast reduction. Accordingly, the output of this method has low brightness and poor contrast. Such artifacts can limit the u of this method in certain image processing applications. Finally, developing an efficient, yet uncomplicated algorithm for contrast enhancement is a
right是什么意思challenging and significant task. However, this task is clearly achieved successfully, as the propod algorithm provided visually pleasing results with no visible artifacts and outperformed the comparable algorithms in terms of visual quality and scored accuracy.
Fig.2. The obtained results of the propod algorithm. (a1 – c1): real-degraded images; (a2 – c2) from left to right: images recovered by the propod algorithm with λ=1.1, λ=1.6, and λ=0.9, respectively.
Fig.3. The obtained results of the propod algorithm. (a1 – c1): real-degraded images; (a2 – c2) from left to right: images re covered by the propod algorithm with λ=0.5, λ=0.4, and λ=0.25, respectively.
images recovered by the propod algorithm with λ=4, λ=1.1, and λ=1.5, respectively.
闺蜜日是几月几号(c) SMQT; (d) GDS; (e) ESIHE; (f) propod algorithm with λ=1.1;
