Recursive sub-image histogram equalization applied to gray scale images

Sim, K. S.; Tso, C.P.; Tan, Yi-Fei

doi:10.1016/j.patrec.2007.02.003

Cited by 450 publications

(211 citation statements)

References 10 publications

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“…Processed sub-image is composed into one image produce the resultant image. Recursive Mean Separate Histogram Equalization (RMSHE) [10] and Recursive sub-image histogram equalization (RSIHE) [11] are the recursive version of BBHE and RSIHE respectively. Minimum mean brightness error bio-histogram equalization (MMBEBHE) [12] uses each gray level for segmenting the histogram of the input image.…”

Section: Different Histogram Equalization (He) Methodsmentioning

confidence: 99%

Contrast Enhancement of Ultrasound Image of Knee Joint Cartilage by Using Multipurpose Beta Optimized Recursive Bi-Histogram Equalization Method

Hossain¹,

Pingguan‐Murphy²,

Hum³

et al. 2015

Proceedings of the 3rd International Conference on Intelligent Systems and Image Processing 2015

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Section: Different Histogram Equalization (He) Methodsmentioning

confidence: 99%

Contrast Enhancement of Ultrasound Image of Knee Joint Cartilage by Using Multipurpose Beta Optimized Recursive Bi-Histogram Equalization Method

Hossain¹,

Pingguan‐Murphy²,

Hum³

et al. 2015

Proceedings of the 3rd International Conference on Intelligent Systems and Image Processing 2015

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“…Dualistic Sub-image Histogram Equalization (DSIHE) [2] and Minimum Mean Brightness Error Bi-histogram Equalization (MMBEBHE) [8]) and techniques that employ multi-histogram equalization (e.g. Recursive Mean-Separate Histogram Equalization (RMSHE) [9], Recursive Sub-image Histogram Equalization (RSIHE) [10], Dynamic Histogram Equalization (DHE) [4] and Brightness Preserving Dynamic Histogram Equalization (BPDHE) [11]). …”

Section: Related Workmentioning

confidence: 99%

An Adaptive Fuzzy Contrast Enhancement Algorithm with Details Preserving

Tang

Isa

2014

J. ICT.Res.Appl.

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Abstract. This paper modifies the Adaptive Contrast Enhancement Algorithm with Details Preserving (ACEDP) technique by integrating a fuzzy element in the image type selection. The proposed technique, named the Adaptive Fuzzy Contrast Enhancement with Details Preserving (AFCEDP) technique, first computes the degree of membership of the input image to three categories, i.e. low-, middle-or high-level images. The AFCEDP technique then clips the histogram at different plateau limits that are computed from both the degree of membership and the clipping functions. The classification of an image in the ACEDP technique is done based solely on the intensity range of the maximum number of pixels, which may be inaccurate. In the proposed AFCEDP technique, the image type classification is handled in a better way with the integration of a fuzzy element. The performance of the proposed AFCEDP technique was compared with the conventional ACEDP technique and several state-of-art techniques described in the literature. The simulation results revealed that the AFCEDP technique demonstrates good capability in contrast enhancement and detail preservation. In addition, the experiments using cervical cell images and HEp-2 cell images showed great potential of the AFCEDP technique as a technique for enhancing medical microscopic images.

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“…They clearly show that higher degree of brightness preservation is required for these images to avoid unpleasant artefacts. While the separation is done only once in BPBHE, RMSHE perform the separation recursively; separate each new histogram further based on their respective means [6][7] [8]. In this case RMSHE produce better result as discussed above.…”

Section: Recursive Mean Separate Histogram Equalization (Rmshe)mentioning

confidence: 99%