Contrast enhancement using brightness preserving bi-histogram equalization
TL;DR: It is shown mathematically that the proposed algorithm preserves the mean brightness of a given image significantly well compared to typical histogram equalization while enhancing the contrast and, thus, provides a natural enhancement that can be utilized in consumer electronic products.
Abstract: Histogram equalization is widely used for contrast enhancement in a variety of applications due to its simple function and effectiveness. Examples include medical image processing and radar signal processing. One drawback of the histogram equalization can be found on the fact that the brightness of an image can be changed after the histogram equalization, which is mainly due to the flattening property of the histogram equalization. Thus, it is rarely utilized in consumer electronic products such as TV where preserving the original input brightness may be necessary in order not to introduce unnecessary visual deterioration. This paper proposes a novel extension of histogram equalization to overcome such a drawback of histogram equalization. The essence of the proposed algorithm is to utilize independent histogram equalizations separately over two subimages obtained by decomposing the input image based on its mean with a constraint that the resulting equalized subimages are bounded by each other around the input mean. It is shown mathematically that the proposed algorithm preserves the mean brightness of a given image significantly well compared to typical histogram equalization while enhancing the contrast and, thus, provides a natural enhancement that can be utilized in consumer electronic products.
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TL;DR: The simulation results indicate that the algorithm can not only enhance the image information effectively but also preserve the original image luminance well enough to make it possible to be used in a video system directly.
Abstract: Histogram equalization is a simple and effective image enhancing technique. But in some conditions, the luminance of an image may be changed significantly after the equalizing process, this is why it has never been utilized in a video system in the past. A novel histogram equalization technique, equal area dualistic sub-image histogram equalization, is put forward in this paper. First, the image is decomposed into two equal area sub-images based on its original probability density function. Then the two sub-images are equalized respectively. Finally, we obtain the results after the processed sub-images are composed into one image. The simulation results indicate that the algorithm can not only enhance the image information effectively but also preserve the original image luminance well enough to make it possible to be used in a video system directly.
1,039 citations
01 May 2007
TL;DR: This dynamic histogram equalization (DHE) technique takes control over the effect of traditional HE so that it performs the enhancement of an image without making any loss of details in it.
Abstract: In this paper, a smart contrast enhancement technique based on conventional histogram equalization (HE) algorithm is proposed. This dynamic histogram equalization (DHE) technique takes control over the effect of traditional HE so that it performs the enhancement of an image without making any loss of details in it. DHE partitions the image histogram based on local minima and assigns specific gray level ranges for each partition before equalizing them separately. These partitions further go though a repartitioning test to ensure the absence of any dominating portions. This method outperforms other present approaches by enhancing the contrast well without introducing severe side effects, such as washed out appearance, checkerboard effects etc., or undesirable artifacts.
892 citations
Cites background from "Contrast enhancement using brightne..."
...Recursive Mean-Separate Histogram Equalization (RMSHE) [5] is another improvement of BBHE....
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...MMBEBHE is the extension of BBHE method that provides maximal brightness preservation....
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...RMSHE enhances the image the best when one level (i.e., BBHE) of recursive partitioning (r = 1) is used....
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...As mentioned earlier, BBHE tries to preserve the average brightness of the image by separating the input image histogram into two parts based on input mean and then equalizing each of the parts independently....
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...BBHE separates the input image histogram into two parts based on input mean....
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TL;DR: Simulation results show that the cases which are not handled well by HE, BBHE and dualistic sub image histogram equalization (DSIHE), can be properly enhanced by MMBEBHE.
Abstract: Histogram equalization (HE) is widely used for contrast enhancement. However, it tends to change the brightness of an image and hence, not suitable for consumer electronic products, where preserving the original brightness is essential to avoid annoying artifacts. Bi-histogram equalization (BBHE) has been proposed and analyzed mathematically that it can preserve the original brightness to a certain extends. However, there are still cases that are not handled well by BBHE, as they require higher degree of preservation. This paper proposes a novel extension of BBHE referred to as minimum mean brightness error bi-histogram equalization (MMBEBHE) to provide maximum brightness preservation. BBHE separates the input image's histogram into two based on input mean before equalizing them independently. This paper proposes to perform the separation based on the threshold level, which would yield minimum absolute mean brightness error (AMBE - the absolute difference between input and output mean). An efficient recursive integer-based computation for AMBE has been formulated to facilitate real time implementation. Simulation results using sample image which represent images with very low, very high and medium mean brightness show that the cases which are not handled well by HE, BBHE and dualistic sub image histogram equalization (DSIHE), can be properly enhanced by MMBEBHE. Besides, MMBEBHE also demonstrate comparable performance with BBHE and DSIHE when come to use the sample images show in [Yeong-Taeg Kim, February 1997] and [Yu Wan et al., October 5 1999].
853 citations
TL;DR: Simulation results show that the cases which are not handled well by HE, BBHE and dualistic sub image histogram equalization (DSIHE), have been properly enhanced by RMSHE.
Abstract: Histogram equalization (HE) is widely used for contrast enhancement. However, it tends to change the brightness of an image and hence, not suitable for consumer electronic products, where preserving the original brightness is essential to avoid annoying artifacts. Bi-histogram equalization (BBHE) has been proposed and analyzed mathematically that it can preserve the original brightness to a certain extend. However, there are still cases that are not handled well by BBHE, as they require higher degree of preservation. This paper proposes a generalization of BBHE referred to as recursive mean-separate histogram equalization (RMSHE) to provide not only better but also scalable brightness preservation. BBHE separates the input image's histogram into two based on its mean before equalizing them independently. While the separation is done only once in BBHE, this paper proposes to perform the separation recursively; separate each new histogram further based on their respective mean. It is analyzed mathematically that the output image's mean brightness will converge to the input image's mean brightness as the number of recursive mean separation increases. Besides, the recursive nature of RMSHE also allows scalable brightness preservation, which is very useful in consumer electronics. Simulation results show that the cases which are not handled well by HE, BBHE and dualistic sub image histogram equalization (DSIHE), have been properly enhanced by RMSHE.
833 citations
TL;DR: An automatic transformation technique that improves the brightness of dimmed images via the gamma correction and probability distribution of luminance pixels and uses temporal information regarding the differences between each frame to reduce computational complexity is presented.
Abstract: This paper proposes an efficient method to modify histograms and enhance contrast in digital images. Enhancement plays a significant role in digital image processing, computer vision, and pattern recognition. We present an automatic transformation technique that improves the brightness of dimmed images via the gamma correction and probability distribution of luminance pixels. To enhance video, the proposed image-enhancement method uses temporal information regarding the differences between each frame to reduce computational complexity. Experimental results demonstrate that the proposed method produces enhanced images of comparable or higher quality than those produced using previous state-of-the-art methods.
795 citations
Cites background or methods from "Contrast enhancement using brightne..."
...4(c) and (d), both the BBHE and DSIHE methods separately equalized the low level and high level of the histogram to solve the problem produced by the THE method....
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...However, its time complexity is still much higher than BBHE and DSIHE....
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...In order to solve the aforementioned problems associated with the THE method, earlier works individually equalized two sub-histograms produced by separation techniques [8], [9]....
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...Therefore, the BBHE, DSIHE, and RSIHE methods failed again to enhance the contrast, as displayed in Fig....
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...Campus Sequence With 600 Frames and 352 × 288 Pixels Per Frame (Statistic Camera) Method THE BBHE DSIHE RSIHE RSWHE DCRGC AWMHE CVC AGCWD Original fps 1190 1172 1181 1167 1158 1165 1152 5 1170 Improved fps 5505 4959 1042 4878 4724 4839 4651 296 4918 Home Sequence With 360 Frames and 480 × 270 Pixels Per Frame (Dynamic Camera) Method THE BBHE DSIHE RSIHE RSWHE DCRGC AWMHE CVC AGCWD Original fps 1198 1190 1187 1179 1169 1172 1162 6 1176 Improved fps 3482 3449 3416 3384 3293 3323 3263 12 3353...
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References
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Book•
11 Sep 1989TL;DR: This text covers the principles and applications of "multidimensional" and "image" digital signal processing and is suitable for Sr/grad level courses in image processing in EE departments.
Abstract: New to P-H Signal Processing Series (Alan Oppenheim, Series Ed) this text covers the principles and applications of "multidimensional" and "image" digital signal processing. For Sr/grad level courses in image processing in EE departments.
2,022 citations
TL;DR: Results of these experiments show that for this particular diagnostic task, there was no significant difference in the ability of the two methods to depict luminance contrast; thus, further evaluation of AHE using controlled clinical trials is indicated.
Abstract: Adaptive histogram equalization (AHE) and intensity windowing have been compared using psychophysical observer studies Experienced radiologists were shown clinical CT (computerized tomographic) images of the chest Into some of the images, appropriate artificial lesions were introduced; the physicians were then shown the images processed with both AHE and intensity windowing They were asked to assess the probability that a given image contained the artificial lesion, and their accuracy was measured The results of these experiments show that for this particular diagnostic task, there was no significant difference in the ability of the two methods to depict luminance contrast; thus, further evaluation of AHE using controlled clinical trials is indicated >
347 citations
05 Aug 1994
TL;DR: In this paper, adaptive histogram equalization was used to enhance the contrast of X-ray chest image and several methods were presented to eliminate the noise caused by the enhancement, the mainly used methods are frame averaging, bandstop zero-phase filtering based on 2D FFT.
Abstract: This paper uses adaptive histogram equalization to enhance the contrast of X-ray chest image. Several methods are presented to eliminate the noise caused by the enhancement. The mainly used methods are frame averaging, bandstop zero-phase filtering based on 2D FFT. The cause of noise is also analyzed.
29 citations