Low-light image enhancement based on normal-light image degradation

Zhao, Bin; Gong, Xiaolin; Wang, Jian; Zhao, Lingchao

doi:10.1007/s11760-021-02093-z

Cited by 4 publications

(4 citation statements)

References 29 publications

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“…Subsequently, the mapping map is adjusted pixel by pixel to obtain the final result. Zhou et al [7] combined the attention mechanism with multi-scale extended pyramid reconstruction to enhance low-light images. The introduction of the attention mechanism facilitated the restoration of image details.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

ZRRD-MBNet：Zero-Reference RETINEX Decomposition-Based Multi-Branch Network for Low-Light Image Enhancement

Liu,

Tong,

Wang

et al. 2024

Preprint

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To address the challenges of low visibility, poor contrast, and significant noise in images captured under various extreme conditions, such as backlighting and low-light situations, we propose a novel zero-reference Retinex decomposition-based multi-branch network known as ZRRD-MBNet for enhancing low-light images. The ZRRD-MBNet is divided into two main components: decomposition and recovery. The decomposition part can separate the input low-light image into three distinct components: the illuminance map, the reflection map, and the noise map. On the other hand, the recovery part follows a dual-path approach. The first path involves gamma transforming the generated illuminance map to enhance brightness effectively. Simultaneously, the second path divides the input low-light image element-wise by the originally decomposed illuminance map. The result is then subtracted element-wise from the noise map obtained during decomposition, yielding a denoised reflection map. To obtain the final enhanced image, the denoised reflectance map is multiplied with the brightness-enhanced illuminance map. The specific loss functions are critical for updating the weight parameters of ZRRD-MBNet and guiding the network during low-light image decomposition. Extensive qualitative and quantitative experiments are conducted using publicly available datasets. The results demonstrate that our method produces high-quality images with clarity, minimal noise, and absence of artifacts.

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Section: Deep Learning Methodsmentioning

confidence: 99%

ZRRD-MBNet：Zero-Reference RETINEX Decomposition-Based Multi-Branch Network for Low-Light Image Enhancement

Liu,

Tong,

Wang

et al. 2024

Preprint

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show abstract

“…There are numerous image processing methods designed to improve image quality by the removal of different kinds of distortions [ 1 , 2 , 3 , 4 , 5 , 6 ]. It is obvious that not every distortion can affect the entire image area; thus, every single pixel does not need to be processed.…”

Section: Introductionmentioning

confidence: 99%

“…distorted and reference images provides information about the pixels/channels that should be corrected (1). The comparison of distorted and corrected images provides knowledge of which pixels/channels are altered by the denoising algorithm (2).…”

mentioning

confidence: 99%

Decomposed Dissimilarity Measure for Evaluation of Digital Image Denoising

Maliński

2023

Sensors

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A new approach to the evaluation of digital image denoising algorithms is presented. In the proposed method, the mean absolute error (MAE) is decomposed into three components that reflect the different cases of denoising imperfections. Moreover, aim plots are described, which are designed to be a very clear and intuitive form of presentation of the new decomposed measure. Finally, examples of the application of the decomposed MAE and the aim plots in the evaluation of impulsive noise removal algorithms are presented. The decomposed MAE measure is a hybrid of the image dissimilarity measure and detection performance measures. It provides information about sources of errors such as pixel estimation errors, unnecessary altered pixels, or undetected and uncorrected distorted pixels. It measures the impact of these factors on the overall correction performance. The decomposed MAE is suitable for the evaluation of algorithms that perform a detection of the distortion that affects only a certain fraction of the image pixels.

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“…At present, many computer vision systems are installed in outdoor environments and lowtemperature water environments. Their performance is easily affected by light distribution [18][19][20][21][22][23][24]. To complete different vision tasks, e.g., object detection, object recognition, and object retrieval, low-light images must be enhanced by different methods to achieve different processing effects.…”

Section: Introductionmentioning

confidence: 99%

Low-Light Image Enhancement and Target Detection Based on Deep Learning

Yao¹

2022

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Most computer vision applications demand input images to meet their specific requirements. To complete different vision tasks, e.g., object detection, object recognition, and object retrieval, low-light images must be enhanced by different methods to achieve different processing effects. The existing image enhancement methods, which are based on non-physical imaging models, and image generation methods, which are based on deep learning, are not ideal for low-light image processing. To solve the problem, this paper explores low-light image enhancement and target detection based on deep learning. Firstly, a simplified expression was constructed for the optical imaging model of low-light images, and a Haze-line was proposed for color correction of low-light images, which can effectively enhance low-light images based on the global background light and medium transmission rate of the optical imaging model of such images. Next, network framework adopted by the proposed low-light image enhancement model was introduced in detail: the framework includes two deep domain adaptation modules that realize domain transformation and image enhancement, respectively, and the loss functions of the model were presented. To detect targets based on the output enhanced image, a joint enhancement and target detection method was proposed for low-light images. The effectiveness of the constructed model was demonstrated through experiments.

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Low-light image enhancement based on normal-light image degradation

Cited by 4 publications

References 29 publications

ZRRD-MBNet：Zero-Reference RETINEX Decomposition-Based Multi-Branch Network for Low-Light Image Enhancement

ZRRD-MBNet：Zero-Reference RETINEX Decomposition-Based Multi-Branch Network for Low-Light Image Enhancement

Decomposed Dissimilarity Measure for Evaluation of Digital Image Denoising

Low-Light Image Enhancement and Target Detection Based on Deep Learning

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