Images captured under poor illumination conditions often exhibit characteristics such as low brightness, low contrast, a narrow gray range, and color distortion, as well as considerable noise, which seriously affect the subjective visual effect on human eyes and greatly limit the performance of various machine vision systems. The role of low-light image enhancement is to improve the visual effect of such images for the benefit of subsequent processing. This paper reviews the main techniques of low-light image enhancement developed over the past decades. First, we present a new classification of these algorithms, dividing them into seven categories: gray transformation methods, histogram equalization methods, Retinex methods, frequency-domain methods, image fusion methods, defogging model methods and machine learning methods. Then, all the categories of methods, including subcategories, are introduced in accordance with their principles and characteristics. In addition, various quality evaluation methods for enhanced images are detailed, and comparisons of different algorithms are discussed. Finally, the current research progress is summarized, and future research directions are suggested.

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An Experiment-Based Review of Low-Light Image Enhancement Methods

This paper presents contrast enhancement algorithms based on fuzzy contextual information of the images. We introduce fuzzy similarity index and fuzzy contrast factor to capture the neighborhood characteristics of a pixel. A new histogram, using fuzzy contrast factor of each pixel is developed, and termed the fuzzy dissimilarity histogram (FDH). A cumulative distribution function is formed with normalized values of an FDH and used as a transfer function to obtain the contrast enhanced image. The algorithm gives good contrast enhancement and preserves the natural characteristic of the image. In order to develop a contextual intensity transfer function, we introduce a fuzzy membership function based on fuzzy similarity index and coefficient of variation of the image. The contextual intensity transfer function is designed using the fuzzy membership function to achieve final contrast enhanced image. The overall algorithm is referred as the fuzzy contextual contrast-enhancement algorithm. The proposed algorithms are compared with the conventional and the state-of-the-art contrast enhancement algorithms. The quantitative and visual assessment of the results is performed. The results of quantitative measures are statistically analyzed using t-test. The exhaustive experimentation and analysis show the proposed algorithm efficiently enhances contrast and yields in natural visual quality images.

Fuzzy-Contextual Contrast Enhancement

Mechanical fault diagnosis and prediction in IoT based on multi-source sensing data fusion

This study presents a new contrast-enhancement approach called entropy-based dynamic sub-histogram equalisation. The proposed algorithm performs a recursive division of the histogram based on the entropy of the sub-histograms. Each sub-histogram is divided recursively into two sub-histograms with equal entropy. A stopping criterion is proposed to achieve an optimum number of sub-histograms. A new dynamic range is allocated to each sub-histogram based on the entropy and number of used and missing intensity levels in the sub-histogram. The final contrast-enhanced image is obtained by equalising each sub-histogram independently. The proposed algorithm is compared with conventional as well as state-of-the-art contrast-enhancement algorithms. The quantitative results for a large image data set are statistically analysed using a paired t-test. The quantitative and visual assessment shows that the proposed algorithm outperforms most of the existing contrast-enhancement algorithms. The proposed algorithm results in natural-looking, good contrast images with almost no artefacts.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ipr.2016.0242

Contrast enhancement using entropy-based dynamic sub-histogram equalisation

Digital chest radiography offers many advantages over filmbased radiography, such as immediate image display, no film processing and room storage, wider dynamic range and lower radiation dose. In general, a raw X-ray image acquired directly from a digital flat detector contains poor quality of image, which may not be suitable for diagnosis and treatment planning. Therefore, a pre-processing technique is usually required to enhance image quality. This paper presents an improved image enhancement on digital chest radiography using the so-called N-CLAHE method, which is based on global and local enhancement. The proposed technique consists of two main steps. Firstly, intensity correction of the raw image is encountered by the log-normalization function which adjusts the intensity contrast of the image dynamically. Secondly, the Contrast Limited Adaptive Histogram Equalization (CLAHE) method is used for enhancing small details, textures and local contrast of the images. The proposed approach was tested using a radiographic survey phantom and a radiographic chest phantom and compared with conventional enhancement methods, such as histogram equalization, unsharp masking, CLAHE. The results show that the proposed N-CLAHE method yields great improvement on the pre-processing correction for digital chest radiography.

Image enhancement on digital x-ray images using N-CLAHE

Image enhancement has an important role in image processing applications. Contrast limited adaptive histogram equalisation (CLAHE) is an effective algorithm to enhance the local details of an image. However, it faces the contrast overstretching and noise enhancement problems. To solve these problems, this study presents a novel image enhancement method, named CLAHE-discrete wavelet transform (DWT), which combines the CLAHE with DWT. The new method includes three main steps: First, the original image is decomposed into low-frequency and high-frequency components by DWT. Then, the authors enhance the low-frequency coefficients using CLAHE and keep the high-frequency coefficients unchanged to limit noise enhancement. This is because the high-frequency component corresponds to the detail information and contains most noises of original image. Finally, reconstruct the image by taking inverse DWT of the new coefficients. To alleviate over-enhancement, the reconstructed and original images are averaged using an originally proposed weighting factor. The weighting operation can control the enhancement levels of regions with different luminances in original image adaptively. This is important because bright parts of image are usually needless to be enhanced in comparison with the dark parts. Extensive experiments show that this method performs well in detail preservation and noise suppression.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ipr.2015.0150

Combination of contrast limited adaptive histogram equalisation and discrete wavelet transform for image enhancement

Contrast enhancement plays an important role in image processing applications. The global histogram equalisation (GHE)-based techniques are very popular for their simpleness. In the author's study, the authors originally divide the GHE techniques into two steps, that is, the pixel populations mergence (PPM) step and the grey-levels distribution (GLD) step. In the PPM step, the pixel populations of adjoining grey scales to be mapped to the same grey scale are merged firstly in input histogram. Then, the new grey scales are redistributed according to a corresponding transformation function in the GLD step. This division is meaningful because the entropy of enhanced image is only determined by pixel populations regardless of grey levels. Then, they prove the entropy of enhanced image is reduced because of mergence. Inspired by GHE, they propose a novel entropy maximisation histogram modiﬁcation scheme, which also consists of PPM and GLD steps. However, the entropy is maximised, that is, the reduction of entropy is minimised under originally presented entropy maximisation rule in their PPM step. In the GLD step, they redistribute the grey scales in the merged histogram using a log-based distribution function to control the enhancement level. Experimental results demonstrate the proposed method is effective.

Entropy maximisation histogram modification scheme for image enhancement

Image registration is an essential process for image fusion, and fusion performance can be used to evaluate registration accuracy. We propose a maximum likelihood (ML) approach to joint image registration and fusion instead of treating them as two independent processes in the conventional way. To improve the visual quality of a fused image, a gradient strength (GS) regularization is introduced in the cost function of ML. The GS of the fused image is controllable by setting the target GS value in the regularization term. This is useful because a larger target GS brings a clearer fused image and a smaller target GS makes the fused image smoother and thus restrains noise. Hence, the subjective quality of the fused image can be improved whether the source images are polluted by noise or not. We can obtain the fused image and registration parameters successively by minimizing the cost function using an iterative optimization method. Experimental results show that our method is effective with transformation, rotation, and scale parameters in the range of [−2.0, 2.0] pixel, [−1.1  deg, 1.1 deg], and [0.95, 1.05], respectively, and variances of noise smaller than 300. It also demonstrated that our method yields a more visual pleasing fused image and higher registration accuracy compared with a state-of-the-art algorithm.

Huang Lidong

Papers

Combination of contrast limited adaptive histogram equalisation and discrete wavelet transform for image enhancement

Entropy maximisation histogram modification scheme for image enhancement

Joint image registration and fusion method with a gradient strength regularization