Dual Autoencoder Network for Retinex-Based Low-Light Image Enhancement

Park, Seonhee; Yu, Shuhui; Kim, Minseo; Park, Kwanwoo; Paik, Joonki

doi:10.1109/access.2018.2812809

Cited by 108 publications

(47 citation statements)

References 20 publications

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“…In this framework, a sparsity regularized reconstruction loss was taken as the loss function, and deep learning based on the self-encoder approach was used to learn the features of image signals acquired under various low-illumination conditions to realize adaptive brightness adjustment and denoising. Park et al proposed a dual autoencoder network model based on Retinex theory [255]; in this model, a stacked autoencoder was combined with a convolutional autoencoder to realize low-light enhancement and noise reduction. The stacked autoencoder, with a small number of hidden units, was used to estimate the smooth illumination component in the space, and the convolutional autoencoder was used to process two-dimensional image information to reduce the amplification of noise during the process of brightness enhancement.…”

Section: G Methods Based On Machine Learningmentioning

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: G Methods Based On Machine Learningmentioning

confidence: 99%

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

et al. 2020

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“…Guo et al proposed a

norm based regularization framework to refine an initial illumination map under a structure-aware prior [ 17 ]. Learning based methods have also been developed for low-light image enhancement [ 53 , 54 , 55 , 56 , 57 , 58 ]. Although visibility and contrast could be increased, these approaches can not handle haze in the scene.…”

Section: Related Workmentioning

confidence: 99%

SIDE—A Unified Framework for Simultaneously Dehazing and Enhancement of Nighttime Hazy Images

Guo

Shi

2020

Sensors

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Single image dehazing is a difficult problem because of its ill-posed nature. Increasing attention has been paid recently as its high potential applications in many visual tasks. Although single image dehazing has made remarkable progress in recent years, they are mainly designed for haze removal in daytime. In nighttime, dehazing is more challenging where most daytime dehazing methods become invalid due to multiple scattering phenomena, and non-uniformly distributed dim ambient illumination. While a few approaches have been proposed for nighttime image dehazing, low ambient light is actually ignored. In this paper, we propose a novel unified nighttime hazy image enhancement framework to address the problems of both haze removal and illumination enhancement simultaneously. Specifically, both halo artifacts caused by multiple scattering and non-uniformly distributed ambient illumination existing in low-light hazy conditions are considered for the first time in our approach. More importantly, most current daytime dehazing methods can be effectively incorporated into nighttime dehazing task based on our framework. Firstly, we decompose the observed hazy image into a halo layer and a scene layer to remove the influence of multiple scattering. After that, we estimate the spatially varying ambient illumination based on the Retinex theory. We then employ the classic daytime dehazing methods to recover the scene radiance. Finally, we generate the dehazing result by combining the adjusted ambient illumination and the scene radiance. Compared with various daytime dehazing methods and the state-of-the-art nighttime dehazing methods, both quantitative and qualitative experimental results on both real-world and synthetic hazy image datasets demonstrate the superiority of our framework in terms of halo mitigation, visibility improvement and color preservation.

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“…Driven by the research on retinex theory-based image enhancement [23]- [27], several works have revealed the potential advantages of using CNNs for low-light image enhancement rather than hand-crafted methods. In [28], [29], a dataset paired with low and bright images was collected for training the retinex network that performs reflectance and illumination decomposition, brightness enhancement, and image denoising. Similarly, a network was used to extract multi-scale retinex features and reconstruct high quality images by discrete wavelet transformation [30].…”

Section: Low-light Enhancementmentioning

confidence: 99%

DeepSelfie: Single-Shot Low-Light Enhancement for Selfies

Kim

Jung

2020

IEEE Access

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Taking a high-quality selfie photo in a low-light environment is challenging. Because the foreground and background often have different illumination conditions, they suffer heavily from over/under-exposure issues and cannot be treated in the same manner when applying image enhancement algorithms. In this work, we propose DeepSelfie, a learning-based image enhancement framework for low-light selfie photos. We address selfie enhancement as a dual-layer image enhancement problem. The foreground and background are thus separately enhanced and combined together via image fusion. To train the selfie enhancement network, we also introduce a method of synthesizing pairs of noisy and dark raw selfie images and their corresponding well-illuminated images. Through extensive experiments of no-reference image quality assessment as well as human subjective evaluation, we show that DeepSelfie provides better results in comparison to several state-of-the-art methods. The code and datasets can be found at https://sites.google.com/view/deepselfie. INDEX TERMS Deep learning, image enhancement, low-light enhancement, selfie.

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Dual Autoencoder Network for Retinex-Based Low-Light Image Enhancement

Cited by 108 publications

References 20 publications

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

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

SIDE—A Unified Framework for Simultaneously Dehazing and Enhancement of Nighttime Hazy Images

DeepSelfie: Single-Shot Low-Light Enhancement for Selfies

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