• Title/Summary/Keyword: Histogram enhancement

Search Result 200, Processing Time 0.021 seconds

Efficient Contrast Enhancement Algorithm using Histogram Stretching (히스토그램 스트레칭을 이용한 효율적인 명암 향상 알고리즘)

  • Kim, Young Ro;Chung, Ji Yung
    • Journal of Korea Society of Digital Industry and Information Management
    • /
    • v.6 no.2
    • /
    • pp.193-198
    • /
    • 2010
  • In this paper, an efficient contrast enhancement algorithm using histogram stretching is proposed. Histogram equalization (HE) and histogram stretching (HS) are effective techniques for contrast enhancement. However, HE and HS result often in excessive contrast enhancement. Proposed technique not only produces better results than those of conventional contrast enhancement techniques, but is also adaptively adjusted to image contents.

Exact Histogram Specification Considering the Just Noticeable Difference

  • Jung, Seung-Won
    • IEIE Transactions on Smart Processing and Computing
    • /
    • v.3 no.2
    • /
    • pp.52-58
    • /
    • 2014
  • Exact histogram specification (EHS) transforms the histogram of an input image into the specified histogram. In the conventional EHS techniques, the pixels are first sorted according to their graylevels, and the pixels that have the same graylevel are further differentiated according to the local average of the pixel values and the edge strength. The strictly ordered pixels are then mapped to the desired histogram. However, since the conventional sorting method is inherently dependent on the initial graylevel-based sorting, the contrast enhancement capability of the conventional EHS algorithms is restricted. We propose a modified EHS algorithm considering the just noticeable difference. In the proposed algorithm, the edge pixels are pre-processed such that the output edge pixels obtained by the modified EHS can result in the local contrast enhancement. Moreover, we introduce a new sorting method for the pixels that have the same graylevel. Experimental results show that the proposed algorithm provides better image enhancement performance compared to the conventional EHS algorithms.

Automatic Contrast Enhancement by Transfer Function Modification

  • Bae, Tae Wuk;Ahn, Sang Ho;Altunbasak, Yucel
    • ETRI Journal
    • /
    • v.39 no.1
    • /
    • pp.76-86
    • /
    • 2017
  • In this study, we propose an automatic contrast enhancement method based on transfer function modification (TFM) by histogram equalization. Previous histogram-based global contrast enhancement techniques employ histogram modification, whereas we propose a direct TFM technique that considers the mean brightness of an image during contrast enhancement. The mean point shifting method using a transfer function is proposed to preserve the mean brightness of an image. In addition, the linearization of transfer function technique, which has a histogram flattening effect, is designed to reduce visual artifacts. An attenuation factor is automatically determined using the maximum value of the probability density function in an image to control its rate of contrast. A new quantitative measurement method called sparsity of a histogram is proposed to obtain a better objective comparison relative to previous global contrast enhancement methods. According to our experimental results, we demonstrated the performance of our proposed method based on generalized measures and the newly proposed measurement.

Weight based Histogram Modification for Contrast Enhancement (명암도 향상을 위한 가중치 기반 히스토그램 수정)

  • Kim, Young-Ro;Dong, Sung-Soo
    • 전자공학회논문지 IE
    • /
    • v.47 no.3
    • /
    • pp.7-13
    • /
    • 2010
  • In this paper, an efficient contrast enhancement algorithm using weighted histogram modification is proposed. For contrast enhancement, histogram equalization (HE) and histogram stretching (HS) are effective techniques. However, HE and HS may have excessive contrast enhancement. Proposed method using weighted histogram modification produces better natural and enhanced results than those of conventional contrast enhancement methods without artifacts.

No Image Contrast Enhancement using Histogram Equalization with Genetic Algorithm (GA를 적용한 히스토그램 평활화 기법에 의한 이미지 대비 향상)

  • Chung, Jin-Wook;Um, Dae-Youn;Kang, Hoon
    • Proceedings of the KIEE Conference
    • /
    • 2004.05a
    • /
    • pp.111-113
    • /
    • 2004
  • Histogram Equalization is the most popular algorithm for contrast enhancement due to its effectiveness and simplicity. In this paper, We propose the advanced contrast enhancement method using genetic algorithm. We propose a novel objective criterion for enhancement, and attempt finding the best image according to the respective criterion. Due to the high complexity of the enhancement criterion proposed, we employ a Genetic Algorithm. We compared our method with other enhancement techniques, like Global Histogram Equalization and Partially Overlapped Sub-Block Histogram Equalization(POSHE).

  • PDF

Automatic Histogram Specification Based on Fuzzy Membership Value for Image Enhancement (퍼지 멤버쉽 값을 이용한 히스토그램 명세화)

  • 황태호;이정훈
    • Proceedings of the Korean Institute of Intelligent Systems Conference
    • /
    • 2002.12a
    • /
    • pp.317-320
    • /
    • 2002
  • In this paper, an automatic histogram specification method is proposed for image enhancement, Fuzzy membership value is adopted for the representation of image histogram. The desired PDF is automatically constructed by the fuzzy membership value. Fuzzy membership value is extracted from dark membership, bright membership function and original histogram. The effectual results are demonstrated by desired PDF which meet the image enhancement requirements. The performance and effectiveness are shown by the analysis and the resultant image in comparison with histogram equalization method.

A Image Contrast Enhancement by Clustering of Image Histogram (영상의 히스토그램 군집화에 의한 영상 대비 향상)

  • Hong, Seok-Keun;Lee, Ki-Hwan;Cho, Seok-Je
    • Journal of the Institute of Convergence Signal Processing
    • /
    • v.10 no.4
    • /
    • pp.239-244
    • /
    • 2009
  • Image contrast enhancement has an important role in image processing applications. Conventional contrast enhancement techniques, histogram stretching and histogram equalization, and many methods based on histogram equalization often fail to produce satisfactory results for broad variety of low-contrast images. So, this paper proposes a new image contrast enhancement method based on the clustering method. The number of cluster of histogram is found by analysing the histogram of original image. The histogram components is classified using K-means algorithm. And then these histogram components are performed histogram stretching and histogram equalization selectively by comparing cluster range with pixel rate of cluster. From the expremental results, the proposed method was more effective than conventional contrast enhancement techniques.

  • PDF

An Adaptive Contrast Enhancement Method using Dynamic Range Segmentation for Brightness Preservation (밝기 보존을 위한 동적 영역 분할을 이용한 적응형 명암비 향상기법)

  • Park, Gyu-Hee;Cho, Hwa-Hyun;Lee, Seung-Jun;Yun, Jong-Ho;Chon, Myung-Ryul
    • The Transactions of the Korean Institute of Electrical Engineers P
    • /
    • v.57 no.1
    • /
    • pp.14-21
    • /
    • 2008
  • In this paper, we propose an adaptive contrast enhancement method using dynamic range segmentation. Histogram Equalization (HE) method is widely used for contrast enhancement. However, histogram equalization method is not suitable for commercial display because it may cause undesirable artifacts due to the significant change in brightness. The proposed algorithm segments the dynamic range of the histogram and redistributes the pixel intensities by the segment area ratio. The proposed method may cause over compressed effect when intensity distribution of an original image is concentrated in specific narrow region. In order to overcome this problem, we introduce an adaptive scale factor. The experimental results show that the proposed algorithm suppresses the significant change in brightness and provides wide histogram distribution compared with histogram equalization.

Histogram Equalization Algorithm for Suppressing Over-Enhancement and Enhancing Edges (과대 대조 강조 방지 및 엣지 강화를 동시에 수행하는 히스토그램 평활화 알고리듬)

  • Mun, Junwon;Kim, Jaeseok
    • Journal of Korea Multimedia Society
    • /
    • v.22 no.9
    • /
    • pp.983-991
    • /
    • 2019
  • Histogram equalization method is a popular contrast enhancement technique. However, there are some drawbacks, namely, over-enhancement, under-enhancement, structure information loss, and noise amplification. In this paper, we propose an edge-enhancing histogram equalization algorithm while suppressing over-enhancement simultaneously. Firstly, over-enhancement is suppressed by clipping a transfer function, then, edge enhancement is achieved by using guided image filter. Experiments are carried out to evaluate the performance of the various HE algorithms. As a result, both qualitative and quantitative assessment showed that the proposed algorithm successfully suppressed over-enhancement while enhancing edges.

Image Contrast Enhancement based on Histogram Decomposition and Weighting (히스토그램 분할과 가중치에 기반한 영상 콘트라스트 향상 방법)

  • Kim, Ma-Ry;Chung, Min-Gyo
    • Journal of Internet Computing and Services
    • /
    • v.10 no.3
    • /
    • pp.173-185
    • /
    • 2009
  • This paper proposes two new image contrast enhancement methods, RSWHE (Recursively Separated and Weighted Histogram Equalization) and RSWHS (Recursively Separated and Weighted Histogram Specification). RSWHE is a histogram equalization method based on histogram decomposition and weighting, whereas RSWHS is a histogram specification method based on histogram decomposition and weighting. The two proposed methods work as follows: 1) decompose an input histogram based on the image's mean brightness, 2) compute the probability for the area corresponding to each sub-histogram, 3) modify the sub-histogram by weighting it with the computed probability value, 4) lastly, perform histogram equalization (in the case of RSWHE) or histogram specification (in the case of RSWHS) on the modified sub-histograms independently. Experimental results show that RSWHE and RSWHS outperform other methods in terms of contrast enhancement and mean brightness preservation as well.

  • PDF