Histogram Equalization using Pixel Value and Cumulative Frequency

보안공학연구지원센터(IJSEIA) 보안공학연구지원센터 2015 International Journal of Software Engineering and Vol.9 No.10

Histogram equalization is an essential technique in Image Processing. It is a contrast adjustment process using image histogram. Contrast upgrade is an imperative zone in image processing for both human and PC vision. Different Methods are proposed and some extraordinary Image Histogram strategies are Adaptive Histogram Equalization system, Recursive mean square histogram adjustment, and Global Histogram balance. In this paper, impressive histogram equalization method by changing mega pixel value is implementing. We consider some of the pixel value between ranges and then calculate its cumulative frequency. After equally distribution intensity is mapped to get a new intensity value a new histogram value.

밀도기반의 분할된 히스토그램 평활화를 통한 대비 향상 기법

윤현섭(Hyunsup Yoon),한영준(Youngjoon Han),한헌수(Hernsoo Hahn) 대한전자공학회 2009 電子工學會論文誌-SC (System and control) Vol.46 No.1

영상에서 밝기의 분포가 밀집된 영역에 포함되는 특징은 구분이 어렵다. 이러한 문제의 해결을 위해 전역 혹은 지역 명암대비 향상기법을 사용하게 되며 주로 히스토그램의 평활화 기법이 적용된다. 기존의 전역 명암대비 향상기법을 적용하는 경우 밝기 밀집 정도를 고려하지 않아서 지나치게 밝거나 너무 어두운 값으로 변환하는 문제를 만들고, 지역 명암대비 향상기법은 결과 영상에서 특징을 분리해버리거나 밝기분포의 불규칙성으로 인해 부자연스러운 영상을 만들어내는 결과를 보여주기도 한다. 본 논문은 이러한 문제를 해결하기 위해 히스토그램을 밀집정도를 기준으로 분할하고, 각 분할된 히스토그램의 평활화 범위를 분할영역의 평균과 분산을 고려하여 결정하는 방법을 제안한다. 제안하는 방법은 평활화를 밀집영역의 밝기범위와 밀집정도를 고려하여 평활화하는 최고 및 최저 밝기를 결정함으로써 지나친 밝기의 변화를 최소화하고, 밀도가 낮은 나머지 영역들에 대해 분리된 평활화를 수행함에 따라 이들 영역의 특징들이 사라지지 않고 향상시키는 효과를 거둘 수 있다. 히스토그램의 분할 및 평활화 범위를 결정하는 방법도 본 논문에서 제시되었다. 제안된 방법의 성능의 우수성은 다양한 밝기 영역을 갖는 실험영상들을 대상으로 기존의 방법들과 비교실험을 통해 입증하였다. In order to enhance the contrast in the regions where the pixels have similar intensities, this paper presents a new histogram equalization scheme. Conventional global equalization schemes over-equalizes those regions so that too bright or dark pixels are resulted and local equalization schemes produce unexpected discontinuities at the boundaries of the blocks. The proposed algorithm segments the original histogram into sub-histograms with reference to brightness level and equalizes each sub-histogram with the limited extents of equalization considering its mean and variance. The final image is determined as the weighted sum of the equalized images obtained by using the sub-histogram equalizations. By limiting the maximum and minimum ranges of equalization operations on individual sub-histograms, the over-equalization effect is eliminated. Also the result image does not miss feature information in low density histogram region since the remaining these area is applied separating equalization. This paper includes how to determine the segmentation points in the histogram. The proposed algorithm has been tested with more than 100 images having various contrast in the images and the results are compared to the conventional approaches to show its superiority.

Improving brightness using Dynamic Fuzzy Histogram Equalization

보안공학연구지원센터(IJSIP) 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.2

This paper proposed brightness preserving dynamic fuzzy histogram equalization using triangular membership function which is the modified technique of histogram equalization. This modified technique, called Brightness Preserving Dynamic Fuzzy Histogram Equalization (BPDFHE), uses fuzzy statistics of digital images for their representation and processing in the fuzzy area which enables the technique to handle the approximation of gray level values in a better way for better presentation. This algorithm enhances image contrast as well as conserves the brightness very well. Some images are not available to excellent quality, so proposed Fuzzy algorithm can be used for image enhancement to improve the quality of the image.

DWTHE: 분할 기반의 히스토그램 평활화

김매리(Mary Kim),정민교(Min Gyo Chung) 한국정보과학회 2009 정보과학회 컴퓨팅의 실제 논문지 Vol.15 No.11

본 논문은 Wang-Ward의 WTHE(Weighted and Thresholded Histogram Equalization) 방법에 히스토그램 분할 개념을 적용한 새로운 영상 화질 개선 방법 (DWTHE: Decomposable WTHE)을 제안한다. DWTHE는 먼저 영상의 평균 밝기 값 또는 명도 균등 분할점을 기준으로 입력 히스토그램의 영역을 분할하고, 분할된 각 영역의 확률밀도 값을 가중치로 사용하여 새로운 히스토그램을 만든 후, 히스토그램 평활화 과정을 수행하게 된다. 하나의 가중치를 사용하는 WTHE 방법과 다르게, 제안 방법은 히스토그램 분할로 인한 복수의 가중치 값을 사용하게 되며, 실험 결과 제안 방법은 기존 방법에 비해 우수한 화질 개선 효과를 보여주었다. Wang and Ward developed an image contrast enhancement method called WTHE (Weighted and Thresholded Histogram Equalization). In this paper, we propose an improved variant of WTHE called DWTHE(Decomposable WTHE) that enhances WTHE through the use of histogram decomposition. Specifically, DWTHE divides an input histogram by using image's mean brightness or equally-spaced brightness points, computes a probability value for each sub-histogram, modifies the sub-histograms by using those probability values as weights, and then performs histogram equalization on the modified input histogram. As opposed to WTHE that uses a single weight, DWTHE uses multiple weights derived from histogram decomposition. Experimental results show that the proposed method outperforms the previous histogram equalization based methods.

히스토그램 확장 및 균등화 기법의 결합을 통한 SVD 기반 영상화질 개선 기법

김동형 사단법인 인문사회과학기술융합학회 2015 예술인문사회융합멀티미디어논문지 Vol.5 No.5

Histogram stretching method, typical image enhancement scheme, enhances image quality by transferring dynamic range of input image to totally available one. It has low complexity, and keep the histogram shape of input image. The amount of enhancement, however, is so different according to dynamic range of input image. Another typical method, histogram equalization method, convert histogram of input image to one with uniform distribution, but there are some artifacts owing to over transformation. The proposed algorithm is image enhance method combining strength of these two methods. Input image is decomposed using SVD(singular value decomposition) in this procedure. After that, we can enhance image via weighted average of singular values of histogram stretching and equalization. Simulation results show that the proposed method yields higher subjective image qualities than other conventional approaches, and also higher entropy that represents variety of pixel values in enhanced images. 히스토그램 확장기법은 입력 이미지의 동적영역을 전체 사용가능한 동적영역으로 변환함으로써 이미지의 화질을 개선하는 기법이다. 낮은 복잡도를 가지며 원본 이미지의 히스토그램 분포의 모양을 그대로 유지하는 장점이 있지만 입력 이미지의 동적영역에 따라 개선정도는 상당한 차이를 보이는 단점이 있다. 또 하나의 대표적인 방법인 히스토그램 균등화기법은 수학적으로 전 동적영역에 대해서 균일한 분포를 가지도록 변환하지만 영상에 따라 과도한 변환으로 인한 화질 열화가 발생할 수 있다는 단점이 있다. 제안하는 알고리즘은 이들 두 방법의 장점을 결합할 수 있는 화질개선 기법으로서 이 과정에서 SVD를 적용하여 입력영상을 분해한다. 이후 히스토그램 확장 및 균등화기법으로 얻어진 이미지의 고유값들과의 가중평균을 통해 개선된 이미지를 얻는다. 실험결과는 제안하는 알고리즘은 히스토그램 균등화기법을 포함하여 기존의 여러 방법과 비교하여 높은 주관적 화질을 보이며 개선영상을 표현하는 화소값의 다양성을 나타내는 엔트로피 또한 상대적으로 높은 것을 볼 수 있다.

A Novel Approach for Brightness Preserving Local Contrast Enhancement based on Distinction Metric

Nyamlkhagva Sengee,Altansukh Sengee,Heung-Kook Choi 한국멀티미디어학회 2011 한국멀티미디어학회 국제학술대회 Vol.2011 No.-

We propose a new extension of bi-histogram equalization called Dualistic Sub-Image Histogram Equalization with Distinction Metric (DSHEDM). DSHEDM consists of two stages. First, large histogram bins that cause washout artifacts are divided into sub-bins using neighborhood metrics; the same intensities of the original image are arranged by neighboring information. In the second stage, the histogram of the original image is separated into two sub-histograms based on the median of the histogram of the original image; the sub-histograms are equalized independently using refined histogram equalization. In an experimental trial, DSHEDM simultaneously preserved the brightness and enhanced the local contrast of the original image.

ENHANCEMENT OF HISTOGRAM EQUALIZATION WITH NEIGHBORHOOD METRICS

Nyamlkhagva Sengee,Tak Yoon O,Kim Tae Yun,Choi Heung Kook 한국멀티미디어학회 2007 한국멀티미디어학회 국제학술대회 Vol.2007 No.-

We present a refinement of histogram equalization using neighborhood metrics with a general framework which orders pixels based on a sequence of sorting functions which uses both global and local information to remap the image greylevels. We designed a novel sorting key with the suggestion of using the original image greylevel as the primary key and a neighborhood voting metric as the secondary key and the novel contrast difference metrics as the third key. We find that our method can provide an improvement in contrast enhancement, can be very flatter histogram. versus global Histogram Equalization (HE) and Histogram equalization with neighborhood metrics (HENM), while avoiding undesirable over-enhancement that can occur with local histogram equalization (LHE) and other methods.

Himawari-8/AHI 기반 True color 영상 생산을 위한 시각화 향상 기법 비교 연구

한현경 ( Hyeon-gyeong Han ),이경상 ( Kyeong-sang Lee ),최성원 ( Sungwon Choi ),서민지 ( Minji Seo ),진동현 ( Donghyun Jin ),성노훈 ( Noh-hun Seong ),정대성 ( Daeseong Jung ),김홍희 ( Honghee Kim ),한경수 ( Kyung-soo Han ) 대한원격탐사학회 2019 大韓遠隔探査學會誌 Vol.35 No.3

True color 영상은 자연색과 유사한 색상이 표출되며 이는 복잡한 지구의 대기 현상 및 지표의 변화에 빠른 모니터링이 가능하다는 장점이 있다. 현재 다양한 기관에서 true color 영상을 생산 중이며 우리나라에서도 차세대 기상위성으로 세대교체가 이루어져 true color 영상 생산의 필요성이 대두되고 있다. 따라서 본 연구에서는 Himawari-8 위성에 탑재된 Advanced Himawari Imager(AHI) 센서의 Top of Atmosphere(TOA) 자료를 이용해 true color 영상 생산을 위한 시각화 향상을 수행하였다. 시각화 향상을 위해 본 연구는Nonlinear enhancement 과 Histogram equalization 두 가지 기법을 각각 수행하였다. 이를 비교해 본 결과, Histogram equalization는 Nonlinear enhancement 대비 Solar Zenith Angle(SZA) 70° 이상 지역과 해양 영역에서 청색 계열이 강한 영상이 나타났으며, Nonlinear enhancement 기법의 경우 Histogram equalization 기법과 비교했을 때 식생 영역이 붉은 특징이 나타났다. True color images display colors similar to natural colors. This has the advantage that it is possible to monitor rapidly the complex earth atmosphere phenomenon and the change of the surface type. Currently, various organizations are producing true color images. In Korea, it is necessary to produce true color images by replacing generations with next generation weather satellites. Therefore, in this study, visual enhancement for true color image production was performed using Top of Atmosphere (TOA) data of Advanced Himawari Imager (AHI) sensor mounted on Himawari-8 satellite. In order to improve the visualization, we performed two methods of Nonlinear enhancement and Histogram equalization. As a result, Histogram equalization showed a strong bluish image in the region over 70° Solar Zenith Angle (SZA) compared to the Nonlinear enhancement and nonlinear enhancement technique showed a reddish vegetation area.

화자 식별에서의 배경화자데이터를 이용한 히스토그램 등화 기법

김명재,양일호,소병민,김민석,유하진 한국음성학회 2012 말소리와 음성과학 Vol.4 No.2

In this paper, we propose a novel approach to improve histogram equalization for speaker identification. Our method collects all speech features of UBM training data to make a reference distribution. The ranks of the feature vectors are calculated in the sorted list of the collection of the UBM training data and the test data. We use the ranks to perform order-based histogram equalization. The proposed method improves the accuracy of the speaker recognition system with short utterances. We use four kinds of speech databases to evaluate the proposed speaker recognition system and compare the system with cepstral mean normalization (CMN), mean and variance normalization (MVN), and histogram equalization (HEQ). Our system reduced the relative error rate by 33.3% from the baseline system.

Optical Illusion using Histogram Analysis

Gwanggil Jeon,Eunju Kim,Nam Kook Kim 보안공학연구지원센터 2015 International Journal of Software Engineering and Vol.9 No.3

In this paper, we propose a fuzzy based histogram equalization method. We adopt Kaufmann’s measure of fuzziness concept for histogram equalization. The main idea of this paper is to use and assess fuzziness. The proposed histogram equalization method is applied to Y channel of YUV, which is transformed signal from RGB image. The obtained and improved Y channel is rejoined to the other I and Q signals and retransformed to RGB image. The objective and the visual performance are compared in simulation results section.