Low-light Image Enhancement Algorithm Based on Retinex and Generative Adversarial Network

Shi, Yangming; Wu, Xiaopo; Zhu, Ming

doi:10.48550/arxiv.1906.06027

Cited by 6 publications

(6 citation statements)

References 38 publications

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“…However, this method is time-consuming and causes noise amplification problems when input images have a higher amount of light loss. The low-light image enhancement technique by Shi et al [10] also used the Retinex method and mixed with a generative adversarial network (GAN) to enhance an image under low-light conditions, although the method caused problems like noisy and overly enhanced results in low-light images, this method proved to be very useful in low-light images with very minimal light where the details of these type of image can be seen clearly. Another application of Retinex is observed in [11], where Retinex is applied in the low-light image for the purpose of autonomous vehicles, and successfully enhances the image illumination and improves the detection of the vehicle yet the problem of time consumed for the algorithm must be considered as the method should be working in real-time.…”

Section: A Retinex Methodsmentioning

confidence: 99%

Enhancement of Low-Light Image using Homomorphic Filtering, Unsharp Masking, and Gamma Correction

Yin¹,

Subaramaniam²,

Shibghatullah³

et al. 2022

IJACSA

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Now-a-days, a digital image can be found almost everywhere, and digital image processing plays a huge role in analyzing and enhancing the image so that it can be delivered in a good condition. Color distortion and loss of image details are the common problems that were faced by low-light image enhancement methods. This paper introduces a low-light image enhancement method that applied the concept of homomorphic filtering, unsharp masking, and gamma correction. The aim of the proposed method is to minimize the two problems stated while producing images of better quality when compared to the other low-light image enhancement methods. An objective evaluation was done on the proposed method, comparing the results produced by the enhanced method with other two existing low-light image enhancement methods. The results obtained showed the proposed method outshines the other two existing low-light image enhancement method in maintaining the image details and producing a natural looking image, achieving the lowest Mean Square Error (MSE) and Lightness Order Error (LOE) scores, and has the highest Features Similarity Index color (FSIMc), Features Similarity Index (FSIM), Structure Similarity Index (SSIM), and Visual information fidelity (VIF) scores. Future studies that should be made on this research are to implement dehaze and denoise functionality into the low-light image as well as enabling it to be applicable in real-time scenarios.

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Section: A Retinex Methodsmentioning

confidence: 99%

Enhancement of Low-Light Image using Homomorphic Filtering, Unsharp Masking, and Gamma Correction

Yin¹,

Subaramaniam²,

Shibghatullah³

et al. 2022

IJACSA

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“…Image algorithm based on deep learning. This method is divided into two main branches, namely, the method based on CNN [19]- [22] and the method based on GAN [23]- [25]. Most CNN-based solutions rely on paired data for supervisory training to achieve good induction and accuracy, such as LLNet [26], KinD [27], MSR-Net [28].…”

Section: Related Workmentioning

confidence: 99%

A Night Image Enhancement Algorithm Based on MDIFE-Net Curve Estimation

Zhang

Dan

et al. 2023

IEICE Trans. Inf. & Syst.

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In order to solve the low-quality problems such as low brightness, poor contrast, noise interference and color imbalance in night images, a night image enhancement algorithm based on MDIFE-Net curve estimation is presented. This algorithm mainly consists of three parts: Firstly, we design an illumination estimation curve (IEC), which adjusts the pixel level of the low illumination image domain through a non-linear fitting function, maps to the enhanced image domain, and effectively eliminates the effect of illumination loss; Secondly, the DCE-Net is improved, replacing the original Relu activation function with a smoother Mish activation function, so that the parameters can be better updated; Finally, illumination estimation loss function, which combines image attributes with fidelity, is designed to drive the no-reference image enhancement, which preserves more image details while enhancing the night image. The experimental results show that our method can not only effectively improve the image contrast, but also make the details of the target more prominent, improve the visual quality of the image, and make the image achieve a better visual effect. Compared with four existing low illumination image enhancement algorithms, the NIQE and STD evaluation index values are better than other representative algorithms, verify the feasibility and validity of the algorithm, and verify the rationality and necessity of each component design through ablation experiments.

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“…Inspired by the adversarial mechanism, some recent works proposed to handle LLIE by using GAN. Retinex-GAN [33] proposed a generator and utilized the converted dataset derived from [6] to execute the paired training manner. Unfortunately, in real-world scenarios, the enhanced results are frequently appeared to be unnatural.…”

Section: Data-driven Deep Learningmentioning

confidence: 99%

Learning Deep Context-Sensitive Decomposition for Low-Light Image Enhancement

Ma¹,

Liu²,

Zhang³

et al. 2021

Preprint

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Enhancing the quality of low-light images plays a very important role in many image processing and multimedia applications. In recent years, a variety of deep learning techniques have been developed to address this challenging task. A typical framework is to simultaneously estimate the illumination and reflectance, but they disregard the scene-level contextual information encapsulated in feature spaces, causing many unfavorable outcomes, e.g., details loss, color unsaturation, artifacts, and so on. To address these issues, we develop a new context-sensitive decomposition network architecture to exploit the scene-level contextual dependencies on spatial scales. More concretely, we build a two-stream estimation mechanism including reflectance and illumination estimation network. We design a novel context-sensitive decomposition connection to bridge the two-stream mechanism by incorporating the physical principle. The spatially-varying illumination guidance is further constructed for achieving the edge-aware smoothness property of the illumination component. According to different training patterns, we construct CSDNet (paired supervision) and CS-DGAN (unpaired supervision) to fully evaluate our designed architecture. We test our method on seven testing benchmarks (including MIT-Adobe FiveK, LOL, ExDark, NPE, etc.) to conduct plenty of analytical and evaluated experiments. Thanks to our designed context-sensitive decomposition connection, we successfully realized excellent enhanced results (with sufficient details, vivid colors, and few noises), which fully indicates our superiority against existing state-of-the-art approaches. Finally, considering the practical needs for high-efficiency, we develop a lightweight CSDNet (named LiteCSDNet) by reducing the number of channels. Further, by sharing an encoder for these two components, we obtain a more lightweight version (SLiteCSDNet for short). SLiteCSDNet just contains 0.0301M parameters but achieves the almost same performance as CSDNet. Code is available at https://github.com/KarelZhang/CSDNet-CSDGAN.

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Low-light Image Enhancement Algorithm Based on Retinex and Generative Adversarial Network

Cited by 6 publications

References 38 publications

Enhancement of Low-Light Image using Homomorphic Filtering, Unsharp Masking, and Gamma Correction

Enhancement of Low-Light Image using Homomorphic Filtering, Unsharp Masking, and Gamma Correction

A Night Image Enhancement Algorithm Based on MDIFE-Net Curve Estimation

Learning Deep Context-Sensitive Decomposition for Low-Light Image Enhancement

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