Adaptive extended piecewise histogram equalisation for dark image enhancement

Ling, Zhigang; Liang, Yan; Wang, Yaonan; Shen, He; Lu, Xiao

doi:10.1049/iet-ipr.2014.0580

Cited by 54 publications

(27 citation statements)

References 19 publications

(19 reference statements)

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“…This kind of method can effectively remove the noise in low-light image enhancement, but there are still the problems of local detail information loss and color distortion. Adaptive extended piecewise HE (AEPHE) [7] divides the original histogram into a set of segmented histograms, and then applies adaptive HE to these extended histograms. The final result of AEPHE is a weighted fusion of these equalized histograms.…”

Section: Related Workmentioning

confidence: 99%

Low-Light Image Enhancement Based on Deep Symmetric Encoder–Decoder Convolutional Networks

Wang

et al. 2020

Symmetry

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A low-light image enhancement method based on a deep symmetric encoder–decoder convolutional network (LLED-Net) is proposed in the paper. In surveillance and tactical reconnaissance, collecting visual information from a dynamic environment and accurately processing that data is critical to making the right decisions and ensuring mission success. However, due to the cost and technical limitations of camera sensors, it is difficult to capture clear images or videos in low-light conditions. In this paper, a special encoder–decoder convolution network is designed to utilize multi-scale feature maps and join jump connections to avoid gradient disappearance. In order to preserve the image texture as much as possible, by using structural similarity (SSIM) loss to train the model on the data sets with different brightness level, the model can adaptively enhance low-light images in low-light environments. The results show that the proposed algorithm provides significant improvements in quantitative comparison with RED-Net and several other representative image enhancement algorithms.

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Section: Related Workmentioning

confidence: 99%

Low-Light Image Enhancement Based on Deep Symmetric Encoder–Decoder Convolutional Networks

Wang

et al. 2020

Symmetry

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“…Some implementations use a transformation function to map the pixel intensity values of an input image to the corresponding intensity values of the enhanced image [21,22,23]. Then the job of the optimization algorithm becomes to optimize the parameters of the transformation function so that it works properly.…”

Section: Transformation Function Optimization (Sho(transformation))mentioning

confidence: 99%

Image Contrast Enhancement using Selfish Herd Optimizer

Guha¹,

Alam²,

Bera³

et al. 2020

Preprint

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Contrast enhancement is an important pre-processing task in any Image Analysis (IA) system. In this paper, we formulate the image contrast enhancement problem as an optimization problem where the goal is to optimize the pixel intensity values of an input image to obtain a contrast enhanced version of the same. This optimization task is executed by suitably customizing a nature-inspired optimization algorithm called Selfish Herd Optimizer (SHO). The optimization problem is solved using two different solution representations: pixel wise optimization (SHO(direct)) and transformation function based optimization (SHO(transformation)). Moreover, an ablation study is performed to select the most appropriate parameters which can be used in tness measure for this optimization problem. On experimenting over the popular Kodak image dataset, it has been observed that the proposed methods outperform many existing methods published recently. Further comparisons indicate that the direct approach performs better than its transformation counterpart. This paper further investigates the robustness of SHO(direct) approach by applying it to enhance the degraded document images of H-DIBCO 2018.

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“…Jabeen et al () proposed a method for image contrast enhancement using weighted transformation function. A method for dark image enhancement was proposed by Ling et al (). Su et al () used quantum‐behaved particle swarm optimization with an adaptive strategy for image enhancement.…”

Section: Introductionmentioning

confidence: 99%

Contrast limited fuzzy adaptive histogram equalization for enhancement of brain images

Magudeeswaran

Singh

2017

Int J Imaging Syst Tech

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Contrast limited fuzzy adaptive histogram equalization (CLFAHE) is proposed to improve the contrast of MRI Brain images. The proposed method consists of three stages. First, the gray level intensities are transformed into membership plane and membership plane is modified with Contrast intensification operator. In the second stage, the contrast limited adaptive histogram equalization is applied to the modified membership plane to prevent excessive enhancement in contrast by preserving the original brightness. Finally, membership plane is mapped back to the gray level intensities. The performance of proposed method is evaluated and compared with the existing methods in terms of qualitative measures such as entropy, PSNR, AMBE, and FSIM. The proposed method provides enhanced results by giving better contrast enhancement and preserving the local information of the original image.

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Adaptive extended piecewise histogram equalisation for dark image enhancement

Cited by 54 publications

References 19 publications

Low-Light Image Enhancement Based on Deep Symmetric Encoder–Decoder Convolutional Networks

Low-Light Image Enhancement Based on Deep Symmetric Encoder–Decoder Convolutional Networks

Image Contrast Enhancement using Selfish Herd Optimizer

Contrast limited fuzzy adaptive histogram equalization for enhancement of brain images

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