Low-light image enhancement using variational optimization-based retinex model

Park, Seonhee; Yu, Shuhui; Moon, Byeongho; Ko, Seungyong; Paik, Joonki

doi:10.1109/tce.2017.014847

Cited by 187 publications

(76 citation statements)

References 18 publications

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“…According to Retinex theory, the human visual system processes information in a specific way during the transmission of visual information, thus removing a series of uncertain factors such as the intensity of the light source and unevenness of light. Consequently, only information that reflects essential characteristics of the object, such as the reflection coefficient, is retained [105]- [109]. Based on the illumination-reflection model (as shown in Fig.…”

Section: Retinex Methodsmentioning

confidence: 99%

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

et al. 2020

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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. INDEX TERMS Review, survey, low-light image enhancement, Retinex method, image enhancement, quality evaluation.

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Section: Retinex Methodsmentioning

confidence: 99%

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

et al. 2020

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“…Histogram equalization is an adaptive enhancement for difference contrast image by adjusting or stretching grey levels into a uniformly distributed histogram. The histogram matching is redistributed the histogram of a specified image according to a given other image [24,25,26,27].Although global approach is suitable for adjusting overall contrast, brightness and distribution of gray scale. However, there are differenet local areas which need to speical enhancing process.…”

Section: Intensity Image Enhancementmentioning

confidence: 99%

A Comprehensive and Practical Image Enhancement Method

2019

KSII TIIS

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Image enhancement is a challenging problem in the field of image processing, especially low-light color images enhancement. This paper proposed a robust and comprehensive enhancement method based several points. First, the idea of bright channel is introduced to estimate the illumination map which is used to attain the enhancing result with Retinex model, and the color constancy is keep as well. Second, in order eliminate the illumination offsets wrongly estimated, morphological closing operation is used to modify the initial estimating illumination. Furthermore, in order to avoid fabricating edges, enlarged noises and over-smoothed visual features appearing in enhancing result, a multi-scale closing operation is used. At last, in order to avoiding the haloes and artifacts presented in enhancing result caused by gradient information lost in previous step, guided filtering is introduced to deal with previous result with guided image is initial bright channel. The proposed method can get good illumination map, and attain very effective enhancing results, including dark area is enhanced with more visual features, color natural and constancy, avoiding artifacts and over-enhanced, and eliminating Incorrect light offsets.

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“…Four possible challenging problems in single image brightening are: 1) noise in under-exposed regions could be amplified; 2) the highlight regions could be washed out; 3) there could be lightness There are two types of single image brightening algorithms. One is model-driven image processing technologies [5], [6], [7], [8], [9], [10], [11], [12] and the other is data-driven methods such as deep learning ones [13], [15]. Inputs to a model-driven image brightening algorithm are an image (images) to be processed and the related visual prior(s) [1].…”

Section: Introductionmentioning

confidence: 99%

Single Image Brightening via Multi-Scale Exposure Fusion With Hybrid Learning

Zheng

Yang

et al. 2021

IEEE Trans. Circuits Syst. Video Technol.

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A small ISO and a small exposure time are usually used to capture an image in back-or low-light condition which results in an image with negligible motion blur and small noise but looks dark. In this paper, a single image brightening algorithm is introduced to brighten such an image. The proposed algorithm includes a unique hybrid learning framework to generate two virtual images with large exposure times. The virtual images are first generated via intensity mapping functions (IMFs) which are computed using camera response functions (CRFs) and this is a model-driven approach. Both the virtual images are then enhanced by using a data-driven approach, i.e. a residual convolutional neural network to approach the ground truth images. The model-driven approach and the data-driven one compensate each other in the proposed hybrid learning framework. The final brightened image is obtained by fusing the original image and two virtual images via a multi-scale exposure fusion algorithm with properly defined weights. Experimental results show that the proposed brightening algorithm outperforms existing algorithms in terms of MEF-SSIM metric.

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Low-light image enhancement using variational optimization-based retinex model

Cited by 187 publications

References 18 publications

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

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

A Comprehensive and Practical Image Enhancement Method

Single Image Brightening via Multi-Scale Exposure Fusion With Hybrid Learning

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