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Proceedings ArticleDOI

A contrast enhancement technique for low light images

10 Mar 2016-Vol. 1715, Iss: 1, pp 020057
TL;DR: A new algorithm for contrast improvement is proposed that reconstructs the enhanced image by applying the inverse DWT and results indicated that the image contrast enhanced by the purposed method was higher than that of the imagesEnhanced by the other conventional state-of-the-art techniques.
Abstract: Digital Imagery systems are traditionally bad in low light conditions. In this paper, a new algorithm for contrast improvement is proposed. The algorithm consists of two stages. The first stage is decomposing the input image into four subbands by applying two-dimensional discrete wavelet transform and estimates the singular value matrix of sub band image. The second stage is that it reconstructs the enhanced image by applying the inverse DWT. The technique is compared with conventional image equalization technique such as standard General Histogram Equalization (GHE) and other state-of-the-art techniques such as Quadrant Dynamic Histogram Equalization (QDHE), Singular-Value-Wavelet based image Equalization (SVWE) and Singular Value Equalization (SVE) on the basis of their Peak Signal to Noise Ratio (PSNR) and Root Mean Square Error (RMSE) values. The simulation results indicated that the image contrast enhanced by the purposed method was higher than that of the images enhanced by the other conventional state-of-the-art techniques.
Citations
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Journal ArticleDOI
TL;DR: A new classification of the main techniques of low-light image enhancement developed over the past decades is presented, 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.
Abstract: 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.

138 citations

Journal ArticleDOI
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5 citations


Cites methods from "A contrast enhancement technique fo..."

  • ...It is utilized to separate the pictures based on the feature of the colour [17]....

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Journal ArticleDOI
TL;DR: In this article , the authors reviewed the latest low-illumination image enhancement methods based on deep learning and divided them into four categories: supervised learning, unsupervised learning, semi-supervised and zero-shot learning methods.
Abstract: As a critical preprocessing technique, low-illumination image enhancement has a wide range of practical applications. It aims to improve the visual perception of a given image captured without sufficient illumination. Conventional low-illumination image enhancement methods are typically implemented by improving image brightness, enhancing image contrast, and suppressing image noise simultaneously. Nevertheless, recent advances in this area are dominated by deep-learning-based solutions, and consequently, various deep neural networks have been proposed and applied to this field. Therefore, this paper briefly reviews the latest low-illumination image enhancement, ranging from its related algorithms to its unsolved open issues. Specifically, current low-illumination image enhancement methods based on deep learning are first sorted out and divided into four categories: supervised learning methods, unsupervised learning methods, semi-supervised learning methods, and zero-shot learning methods. Then, existing low-light image datasets are summarized and analyzed. In addition, various quality assessment indices for low-light image enhancement are introduced in detail. We also compare 14 representative algorithms in terms of both objective evaluation and subjective evaluation. Finally, the future development trend of low-illumination image enhancement and its open issues are summarized and prospected.

2 citations

Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper proposed a 4D-NLM (non-local means) denoising method for 4D fetal heart ultrasound image sequence, which takes advantage of context similar information in neighboring images to denoise the target image, and an enhancing method called the Adaptive Clipping for Each Histogram Pillar (ACEHP), which is designed to enhance myocardial spaces to distinguish them from blood spaces.
Abstract: Background 4D ultrasound images of human fetal heart are important for medical applications such as evaluation of fetal heart function and early diagnosis of congenital heart diseases. However, due to the high noise and low contrast characteristics in fetal ultrasound images, denoising and enhancements are important. Methods In this paper, a special method framework for denoising and enhancing is proposed. It consists of a 4D-NLM (non-local means) denoising method for 4D fetal heart ultrasound image sequence, which takes advantage of context similar information in neighboring images to denoise the target image, and an enhancing method called the Adaptive Clipping for Each Histogram Pillar (ACEHP), which is designed to enhance myocardial spaces to distinguish them from blood spaces. Results Denoising and enhancing experiments show that 4D-NLM method has better denoising effect than several classical and state-of-the-art methods such as NLM and WNNM. Similarly, ACEHP method can keep noise level low while enhancing myocardial regions better than several classical and state-of-the-art methods such as CLAHE and SVDDWT. Furthermore, in the volume rendering after the combined "4D-NLM+ACEHP" processing, the cardiac lumen is clear and the boundary is neat. The Entropy value that can be achieved by our method framework (4D-NLM+ACEHP) is 4.84. Conclusions Our new framework can thus provide important improvements to clinical fetal heart ultrasound images.

1 citations

References
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Journal ArticleDOI
TL;DR: A block-overlapped histogram equalization system for enhancing contrast of image sequences and has various applications such as video door phone, security video cameras, in addition to the original target video camcorders.
Abstract: In this paper we propose a block-overlapped histogram equalization system for enhancing contrast of image sequences. The proposed system has various applications such as video door phone, security video cameras, in addition to the original target video camcorders.

430 citations

Journal ArticleDOI
TL;DR: In this letter, a new satellite image contrast enhancement technique based on the discrete wavelet transform (DWT) and singular value decomposition has been proposed and it reconstructs the enhanced image by applying inverse DWT.
Abstract: In this letter, a new satellite image contrast enhancement technique based on the discrete wavelet transform (DWT) and singular value decomposition has been proposed. The technique decomposes the input image into the four frequency subbands by using DWT and estimates the singular value matrix of the low-low subband image, and, then, it reconstructs the enhanced image by applying inverse DWT. The technique is compared with conventional image equalization techniques such as standard general histogram equalization and local histogram equalization, as well as state-of-the-art techniques such as brightness preserving dynamic histogram equalization and singular value equalization. The experimental results show the superiority of the proposed method over conventional and state-of-the-art techniques.

310 citations

Journal ArticleDOI
TL;DR: A novel approach for shape preserving contrast enhancement is presented by means of a local histogram equalization algorithm which preserves the level-sets of the image.
Abstract: A novel approach for shape preserving contrast enhancement is presented in this paper. Contrast enhancement is achieved by means of a local histogram equalization algorithm which preserves the level-sets of the image. This basic property is violated by common local schemes, thereby introducing spurious objects and modifying the image information. The scheme is based on equalizing the histogram in all the connected components of the image, which are defined based both on the grey-values and spatial relations between pixels in the image, and following mathematical morphology, constitute the basic objects in the scene. We give examples for both grey-value and color images.

178 citations

Journal ArticleDOI
TL;DR: Simulation results show that the proposed GC-CHE method outperforms existing histogram-based methods, such as HE, BHE, and RMSHE, in various situations.
Abstract: Histogram equalization is a simple and effective method for contrast enhancement as it can automatically define the intensity transformation function based on statistical characteristics of the image. However, it tends to alter the brightness of the entire image, which it is not suitable for consumer electronic products, where preservation of the original brightness is essential to avoid annoying artifacts. This paper presents a new contrast enhancement method for generalization of the existing bihistogram equalization (BHE) and recursive mean-separate histogram equalization (RMSHE) methods. The proposed method is referred to gain-controllable clipped histogram equalization (GC-CHE) to provide both histogram equalization and brightness preservation. More specifically adaptive contrast enhancement is realized by using clipped histogram equalization with controllable gain. The clipping rate is determined based on the mean brightness, and the clipping threshold is determined based on the clipping rate. The clipping rate is adaptively controlled to enhance the contrast with preserving the mean brightness. It is mathematically proven that the mean brightness of the output image converges to that of the input image with adaptive controlled. Simulation results show that the proposed GC-CHE method outperforms existing histogram-based methods, such as HE, BHE, and RMSHE, in various situations.

177 citations

Journal ArticleDOI
TL;DR: The proposed QDHE method outperforms some methods existing in literature by producing clearer enhanced images without any intensity saturation, noise amplification, and over-enhancement, and is suitable for images captured in low-light environments.
Abstract: In this paper, we introduce a histogram equalization (HE)-based technique, called quadrant dynamic histogram equalization (QDHE), for digital images captured from consumer electronic devices. Initially, the proposed QDHE algorithm separates the histogram into four (quadrant) sub-histograms based on the median of the input image. Then, the resultant sub-histograms are clipped according to the mean of intensity occurrence of input image before new dynamic range is assigned to each sub-histogram. Finally, each sub-histogram is equalized. Based on extensive simulation results, the QDHE method outperforms some methods existing in literature, which can be considered as state-of-the-arts, by producing clearer enhanced images without any intensity saturation, noise amplification, and over-enhancement. Furthermore, image details of the processed image are well preserved and highlighted. For this reason, the proposed QDHE algorithm is suitable for images captured in low-light environments - an unavoidable situation by many consumer electronics products such as camera devices in cell phone.

141 citations