Optimized Contrast Enhancement for Infrared Images Based on Global and Local Histogram Specification

Li, Chengwei; Sui, Xiubao; Kuang, Xiaodong; Liu, Yuan; Gu, Guohua; Chen, Qian

doi:10.3390/rs11070849

Cited by 22 publications

(10 citation statements)

References 41 publications

(61 reference statements)

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“…(1) Medical image enhancement algorithm based on the spatial domain: Histogram equalization (HE) is the most typical spatial domain enhancement method [15] that could quickly and intuitively improve image contrast. In accordance with the different enhancement regions, the histogram could be divided into global HE (GHE) [16][17][18][19] and local HE (LHE) [20][21][22][23][24]. GHE has low computational complexity and good brightness retention effect.…”

Section: Related Workmentioning

confidence: 99%

MIE-NSCT: Adaptive MRI Enhancement Based on Nonsubsampled Contourlet Transform

Liu

Zhao

Dong

et al. 2021

Mathematical Problems in Engineering

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Image enhancement technology is often used to improve the quality of medical images and helps doctors or expert systems identify and diagnose diseases. This paper aimed at the characteristics of magnetic resonance imaging (MRI) with complex and difficult-to-enhance details and to propose a nonsubsampled contourlet transform- (NSCT-) based enhancement algorithm called MIE-NSCT. NSCT was used for MRI sub-band decomposition. For high-pass sub-bands, four fuzzy rules were proposed to enhance multiscale and multidirectional edge contour details from adjacent eight directions, whilst for low-pass sub-bands, a new adaptive histogram enhancement algorithm was proposed. The problem of noise amplification and loss of details during the enhancement process was solved. The algorithm was verified on the public dataset BraTS2017 and compared with other advanced methods. Experimental results showed that MIE-NSCT had obvious advantages in improving the quality of medical images, and high-quality medical images showed enhanced performance in grading tumour. MIE-NSCT is suitable for integration into an interactive expert system to provide support for the visualization of disease diagnosis.

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Section: Related Workmentioning

confidence: 99%

MIE-NSCT: Adaptive MRI Enhancement Based on Nonsubsampled Contourlet Transform

Liu

Zhao

Dong

et al. 2021

Mathematical Problems in Engineering

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show abstract

“…This method can produce almost strict ordering of the pixels of the input image and assign it to the desired grayscale [28]. In addition, the local histogram is normalized, and the global histogram is applied to the segmented local image blocks to perform image enhancement [29], [30]. However, the local HS is an operation that relies on sliding windows, and the additional generation of the checkerboard effect is still an urgent problem to be solved [31].…”

Section: Related Workmentioning

confidence: 99%

HS–GS: A Method for Multicenter MR Image Standardization

et al. 2020

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The access to and sharing of medical image data is essential to accelerate the research progress of complex diseases and sudden disease outbreaks. Multicenter image data is collected from different medical institutions, and the contrast and brightness of the images are significantly different, making it difficult to use the images directly. Herein, we introduce a standardized method based on magnetic resonance imaging, referred to as Histogram specification-grid search (HS-GS), which is mainly used to eliminate differences in image contrast and brightness. A Gaussian probability density function with adjustable parameters is used to generate the cumulative distribution function, and the transfer function required for the HS mapping is constructed to obtain standardized image sets based on the controllable parameters. The image sets are used to perform the GS task of radiomics classification to find the optimal controllable parameter combination and classification results, and then obtain the optimal standardized image sets. We used HS-GS to test and verify the predictive ability of the standardized mixed image sets for glioma grading, and compared it with existing methods. The experiments indicate that the standardized image sets generated by the HS-GS algorithm retain excellent stability after mixing and also show excellent classification performance. This novel image set standardization technique has proven to be a promising solution for integration into medical expert systems.

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“…At present, single-channel image enhancement methods mainly include spatial domain algorithms and frequency domain algorithms [6][7][8]. Common spatial domain algorithms include histogram matching [9][10][11], Retinex algorithms [12][13][14][15], morphological methods [16], differential filtering algorithms, dark channel prior algorithms, and deep learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

Multispectral Image Enhancement Based on the Dark Channel Prior and Bilateral Fractional Differential Model

et al. 2022

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Compared with single-band remote sensing images, multispectral images can obtain information on the same target in different bands. By combining the characteristics of each band, we can obtain clearer enhanced images; therefore, we propose a multispectral image enhancement method based on the improved dark channel prior (IDCP) and bilateral fractional differential (BFD) model to make full use of the multiband information. First, the original multispectral image is inverted to meet the prior conditions of dark channel theory. Second, according to the characteristics of multiple bands, the dark channel algorithm is improved. The RGB channels are extended to multiple channels, and the spatial domain fractional differential mask is used to optimize the transmittance estimation to make it more consistent with the dark channel hypothesis. Then, we propose a bilateral fractional differentiation algorithm that enhances the edge details of an image through the fractional differential in the spatial domain and intensity domain. Finally, we implement the inversion operation to obtain the final enhanced image. We apply the proposed IDCP_BFD method to a multispectral dataset and conduct sufficient experiments. The experimental results show the superiority of the proposed method over relative comparison methods.

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Optimized Contrast Enhancement for Infrared Images Based on Global and Local Histogram Specification

Cited by 22 publications

References 41 publications

MIE-NSCT: Adaptive MRI Enhancement Based on Nonsubsampled Contourlet Transform

MIE-NSCT: Adaptive MRI Enhancement Based on Nonsubsampled Contourlet Transform

HS–GS: A Method for Multicenter MR Image Standardization

Multispectral Image Enhancement Based on the Dark Channel Prior and Bilateral Fractional Differential Model

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