Retinal fundus image enhancement with image decomposition and visual adaptation

Wang, Jianglan; Li, Yongjie; Yang, Kai-Fu

doi:10.1016/j.compbiomed.2020.104116

Cited by 39 publications

(16 citation statements)

References 35 publications

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“…SSR [31], MSR [32], MSRCR [33] regard the illumination as interference, these methods normalize the separated reflection as the enhancement result, but directly discard the illumination will easily make local over-enhancing of image color, resulting in a color cast in the enhanced image. Another strategy [34][35][36] is first adjusting the illumination and then obtain the enhanced results by element-wise multiplication of the adjusted illumination and reflectance, which is more accordance with the Retinex theory. However, these methods utilize hand-crafted priors for the underconstrained decomposition problem and may not generate well results for various low light images.…”

Section: A Illumination Enhancementmentioning

confidence: 81%

γ Radiation Image Enhancement Method Based on Non-Linear Mapping

et al. 2022

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For the problems of darkness, insufficient contrast, and color cast in the images captured by CMOS image sensors in the γ radiation environment, this paper proposed a joint contrast improvement and color cast correction approach for the γ radiation image enhancement, which improved the image effects from enhancing detail expression and color expression. For the problem of overall darkness of γ radiation image, we firstly utilized the logarithmic mapping to improve the image brightness. Secondly, for the insufficient contrast of image details, a contrast enhancement method with an adaptive Gamma coefficient is proposed, which adaptively adjusts the image brightness at the pixel level, so that the image detail expression is more in line with the human visual characteristics (HVC). Lastly, the color level remapping method is exploited for color cast correction. Extensive experiments are conducted on the γ radiation images collected in the Co60 environment. With the help of the proposed method, we can achieve the best results in quantitative and visual comparison. Experimental results demonstrate that the proposed method enjoys state-of-the-art performance in γ radiation image enhancement.INDEX TERMS γ radiation image, image enhancement, non-linear mapping, contrast improvement, color cast correction.

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Section: A Illumination Enhancementmentioning

confidence: 81%

γ Radiation Image Enhancement Method Based on Non-Linear Mapping

et al. 2022

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“…The contrast enhancement measurement (CEM), this non-reference scale measures detail and contrast in images that is given by [21]: The natural image quality evaluator (NIQE), is a non-reference scale that measures the amount of color detail in an image, the lower its value, the higher the color information, this scale is mathematically based on statistical features and the multivariate gaussian model (MVG). The properties of each part of the image are calculated after it is divided into several regions with constant size.…”

Section: Quality Assessmentmentioning

confidence: 99%

Enhancement of the Retinal Images Based on Wavelet Fusion Technique Using Adaptive Histogram Equalization Method

2022

IJIES

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Colour retinal image enhancement plays an important role in eye diseases and classification techniques. Optical coherence tomography devices are used to take images, which often suffer from a lack of lighting and contrast. In this study, a new algorithm for improving colour retinal images was suggested. The suggested algorithm was developed on the basis of improving the lighting (L) based on (Lab) colour space while preserving the colour information (a and b). The lighting enhancement included the wavelet fusion between the original lighting component and the improved component twice using contrast-limited adaptive histogram equalisation (CLAHE). Data containing 40 image forms (driver rental images dataset) were used, and the proposed method of improvement was compared with several other methods, such as principal component analysis using reflection model (PCAURM), luminosity and contrast adjustment (LCA), fuzzy logic by stretch membership function (FLSMF), CLAHE, contrast enhancement approach (CEA) and modified color histogram equalization (MCHE). The collected results showed that the proposed method obtained excellent quality rates in terms of natural image quality evaluator (4.680), entropy (6.985), contrast enhancement measurement (0.569) and structural similarity index (0.963).

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“…At present, for the low-level visual feature processing of images based on bionic vision, many related theories have been proposed and good experimental results have been obtained [1][2][3]. The edges and contours are the dominant features to describe an image; hence, these two features are usually employed for higher-level image processing.…”

Section: Introductionmentioning

confidence: 99%

“…Compared with the Laplace operator, although the noise in the image is suppressed, it weakens some low-intensity edges and causes a discontinuity in edge detection. (2) Another is a method based on the transform domain, which transforms the image to the corresponding transform domain through various image transformations, obtains the coefficient matrix, and performs a certain correction on the coefficient matrix to obtain the result. For example, wavelet transform [14] uses the transformed high-frequency components to eliminate the sudden change information and noise in the image.…”

Section: Introductionmentioning

confidence: 99%

A Novel Image Edge Detection Method Based on the Asymmetric STDP Mechanism of the Visual Path

Fang

Yuan

Yin

et al. 2022

Wireless Communications and Mobile Computing

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The detection of image edges plays an important role for image processing. In view of the fact that these existing methods cannot effectively detect the edge of the image when facing the image with rich details. This paper proposes a novel method of asymmetric spike-timing-dependent plasticity (STDP) image edge detection based on the visual physiological mechanism. In the proposed method, the original image is preprocessed by the Gabor filter to simulate the visual physiological orientation characteristics to obtain the image information in different directions, and the orientation feature fusion is used to reconstruct the primary edge feature information of the image. Then, based on the mechanism of the visual nervous system, a neuron network composed of dynamic synapses based on the asymmetric STDP mechanism is constructed to further process it to obtain impulse response images. In order to eliminate disturbance of the neuron’s system noise on the impulse response image, the impulse response image is filtered by a Gaussian filter. Then, the lateral inhibition between neurons is applied to refine the filtered image edges. Finally, the result is normalized, and the final edge of the experimental image is obtained. Experimental results based on the colony image data set collected in the laboratory indicate that the proposed method achieved better performance than these state-of-the-art methods; meanwhile, the AUC value remains above 0.6.

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Retinal fundus image enhancement with image decomposition and visual adaptation

Cited by 39 publications

References 35 publications

γ Radiation Image Enhancement Method Based on Non-Linear Mapping

γ Radiation Image Enhancement Method Based on Non-Linear Mapping

Enhancement of the Retinal Images Based on Wavelet Fusion Technique Using Adaptive Histogram Equalization Method

A Novel Image Edge Detection Method Based on the Asymmetric STDP Mechanism of the Visual Path

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