Image Enhancement via Subimage Histogram Equalization Based on Mean and Variance

Zhuang, Liyun; Guan, Yuanyuan

doi:10.1155/2017/6029892

Cited by 35 publications

(37 citation statements)

References 31 publications

(46 reference statements)

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“…From 192 is the variance of Otsu's [16] method for given , evolution based optimization techniques used to find intensity level ( ) for maximizing objective function as given in (20).…”

Section: B Computation Of Threshold Level(otsu's Method)mentioning

confidence: 99%

“…There is major a problem exits in the images taken for remote sensing applications from satellites, for medical images also [16], the reflectance values of sensors not matching abilities of the colour or monochrome display of a system. Materials or any other structures on the Earth emit and reflects different energy levels.…”

Section: A Introductionmentioning

confidence: 99%

“…The Contrast Enhancement[16]-[17] is a technique of changing the pixel value ranges in a images to in order to increase contrast. From various methods available methods for image enhancement, Histogram Equalization (HE) is widespread method specially for low contrast medical and satellite image enhancement, this HE can also be used for speech recognition, object tracking, etc.…”

mentioning

confidence: 99%

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Segmentation of Low Contrast Satellite and Medical Image s Based on Level Set Function with Harmony Search Optimization Algorithm

Srikanth¹,

Bikshalu²

2019

IJEAT

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The main aim of digital image segmentation for portioned the image in to its constituents parts for getting information regarding features of image also used to get pathological details from medical images. The literature available from last two decades the important scheme for image segmentation is with Level Set technique, multilevel thresholding of gray scale on histogram of image is also a traditional method of image segmentation. In this paper low contrast images from medical and satellite images considered for image segmentation to extract features. This paper puts forward a novel image segmentation method via Level Set Function along with BiHistogram Equalization based on Harmony Search Algorithm(LSFBHEHS). The Selective Binary and Gaussian Filtering Regularised Level Set (SBGFRLS) is efficient novel region based Active Contour Model, it uses a novel region-based signed pressure force (SPF) function, it can adeptly halt the contours at blurred edges and weak edges. Other important advantage is internal and external boundaries can be distinguished by fixing the initial contour may be anyplace in the considered image. This method is resourceful but requires more time and inefficient for segmentation of low contrast images. This problem is rectified by applying bi-histogram equalization(BHE) image enhancement method prior to Level Set, it can be treated as pre-processing. In BHE technique of image enhancement, the image histogram is partitioned into two divisions based optimized gray level threshold , and equalize each part of histogram separately and combined later. To find the optimized threshold level to slice the histogram into two parts, Otsu’s multilevel thresholding method used to find threshold level, to find optimized thresholding level Harmony Search Algorithm(HSA) is implemented to maximize inert class variance as objective function. For evaluating the proposed method and SBGFRLS, the qualitative measured used like Dice similarity index, Measure of Enhancement(EME) and time required, for experimentation numerous low contrast satellite and medical images are considered, results clarified that the proposed method is more efficient for low contrast and inhomogeneous images.

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“…From 192 is the variance of Otsu's [16] method for given , evolution based optimization techniques used to find intensity level ( ) for maximizing objective function as given in (20).…”

Section: B Computation Of Threshold Level(otsu's Method)mentioning

confidence: 99%

Section: A Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Segmentation of Low Contrast Satellite and Medical Image s Based on Level Set Function with Harmony Search Optimization Algorithm

Srikanth¹,

Bikshalu²

2019

IJEAT

View full text Add to dashboard Cite

show abstract

“…However, their approach does not adjust the level of enhancement, thereby resulting in darker or brighter enhanced images. Zhuang and Guan [17] used the mean and variance-based sub-image histogram equalization method to increase the contrast of the input image with brightness while retaining important features. However, because some IETs produce over-enhanced images and artifacts, Hussain et al [8] proposed a dark image enhancement approach where local transformation of the image pixels is considered.…”

Section: Introductionmentioning

confidence: 99%

A new evaluation function for face image enhancement in unconstrained environments using metaheuristic algorithms

Oloyede

Hancke

Myburgh

et al. 2019

J Image Video Proc.

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Image enhancement is an integral component of face recognition systems and other image processing tasks such as in medical and satellite imaging. Among a number of existing image enhancement methods, metaheuristic-based approaches have gained popularity owing to their highly effective performance rates. However, the need for improved evaluation functions is a major research concern in the study of metaheuristic-based image enhancement methods. Thus, in this paper, we present a new evaluation function for improving the performance of metaheuristic-based image enhancement methods. Essentially, we applied our new evaluation function in conjunction with metaheuristic-based optimization algorithms in order to select automatically the best enhanced face image based on a linear combination of different key quantitative measures. Furthermore, different from other existing evaluation functions, our evaluation function is finitely bounded to determine easily whether an image is either too dark or too bright. This makes it better suited to find optimal solutions (best enhanced images) during the search process. Our method was compared with existing metaheuristic-based methods and other state-of-the-art image enhancement techniques. Based on the qualitative and quantitative measures obtained, our approach is shown to enhance facial images in unconstrained environments significantly.

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“…Histogram correction is a commonly used technique in image enhancement [7]. Among them, histogram equalization (HE) and normalization techniques [8,9] are often used to adjust the overall brightness of images. is type of methods mainly expands the range of gray value in the histogram without adjusting its intensity.…”

Section: Introductionmentioning

confidence: 99%

Automatic Histogram Specification for Glioma Grading Using Multicenter Data

Chen

Zhao

et al. 2019

Journal of Healthcare Engineering

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Multicenter sharing is an effective method to increase the data size for glioma research, but the data inconsistency among different institutions hindered the efficiency. This paper proposes a histogram specification with automatic selection of reference frames for magnetic resonance images to alleviate this problem (HSASR). The selection of reference frames is automatically performed by an optimized grid search strategy with coarse and fine search. The search range is firstly narrowed by coarse search of intraglioma samples, and then the suitable reference frame in histogram is selected by fine search within the sample selected by coarse search. Validation experiments are conducted on two datasets GliomaHPPH2018 and BraTS2017 to perform glioma grading. The results demonstrate the high performance of the proposed method. On the mixed dataset, the average AUC, accuracy, sensitivity, and specificity are 0.9786, 94.13%, 94.64%, and 93.00%, respectively. It is about 15% higher on all indicators compared with those without HSASR and has a slight advantage over the result of a manually selected reference frame by radiologists. Results show that our methods can effectively alleviate multicenter data inconsistencies and lift the performance of the prediction model.

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Image Enhancement via Subimage Histogram Equalization Based on Mean and Variance

Cited by 35 publications

References 31 publications

Segmentation of Low Contrast Satellite and Medical Image s Based on Level Set Function with Harmony Search Optimization Algorithm

Segmentation of Low Contrast Satellite and Medical Image s Based on Level Set Function with Harmony Search Optimization Algorithm

A new evaluation function for face image enhancement in unconstrained environments using metaheuristic algorithms

Automatic Histogram Specification for Glioma Grading Using Multicenter Data

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