Reference-free low-light image enhancement by associating hierarchical wavelet representations

Yang, Xuemin; Gong, Junhong; Wu, Lianpei; Yang, Zhijing; Shi, Yukai; Nie, Feiping

doi:10.1016/j.eswa.2022.118920

Cited by 7 publications

(2 citation statements)

References 39 publications

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“…In this section, we compare our method with several state-of-the-art low-light image enhancement methods. These methods include one conventional method (Vevid [12]), three supervised methods(KinD++ [26], Restormer [38], LACN [8]), and four unsupervised methods (Zero-DCE++ [6], Reference-freeLLIE [46], EnlightGAN [11], LE-GAN [35]). To demonstrate the robustness of our proposed method, we give more experiments on crossdatasets.…”

Section: Quantitative Evaluationmentioning

confidence: 99%

Unsupervised Low-Light Image Enhancement via Virtual Diffraction Information in Frequency Domain

Zhang

Qin

et al. 2023

Remote Sensing

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With the advent of deep learning, significant progress has been made in low-light image enhancement methods. However, deep learning requires enormous paired training data, which is challenging to capture in real-world scenarios. To address this limitation, this paper presents a novel unsupervised low-light image enhancement method, which first introduces the frequency-domain features of images in low-light image enhancement tasks. Our work is inspired by imagining a digital image as a spatially varying metaphoric “field of light”, then subjecting the influence of physical processes such as diffraction and coherent detection back onto the original image space via a frequency-domain to spatial-domain transformation (inverse Fourier transform). However, the mathematical model created by this physical process still requires complex manual tuning of the parameters for different scene conditions to achieve the best adjustment. Therefore, we proposed a dual-branch convolution network to estimate pixel-wise and high-order spatial interactions for dynamic range adjustment of the frequency feature of the given low-light image. Guided by the frequency feature from the “field of light” and parameter estimation networks, our method enables dynamic enhancement of low-light images. Extensive experiments have shown that our method performs well compared to state-of-the-art unsupervised methods, and its performance approximates the level of the state-of-the-art supervised methods qualitatively and quantitatively. At the same time, the light network structure design allows the proposed method to have extremely fast inference speed (near 150 FPS on an NVIDIA 3090 Ti GPU for an image of size 600×400×3). Furthermore, the potential benefits of our method to object detection in the dark are discussed.

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Section: Quantitative Evaluationmentioning

confidence: 99%

Unsupervised Low-Light Image Enhancement via Virtual Diffraction Information in Frequency Domain

Zhang

Qin

et al. 2023

Remote Sensing

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“…Li et al [63,64] enhanced low-illumination images by estimating their high-order curves, which could reconstruct high-quality images efficiently. Besides, the progressive self-enhancement [65] and hierarchical wavelet representations [66] was injected into lowlight image enhancement. Although learning-based methods can enhance the low illumination images, they have high complexity and consume computing resources.…”

Section: Introductionmentioning

confidence: 99%

Retinex‐inspired contrast stretch and detail boosting for lowlight image enhancement

Liu

Lan

et al. 2023

IET Image Processing

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Lowlight images with low brightness and contrast, blurry details usually bring us an uncomfortable visual experience. To promote the quality of these deviation images, this paper presents a new and efficient approach, named MFMR, for enhancing lowlight images in the hue‐saturation‐value (HSV) colour space. Concretely, the multi‐angle filter is first applied to estimate the artifact‐free illumination and reflection component of the V‐channel. Afterward, the adaptive bi‐interval histogram with human visual characteristics and morphological operations is employed to process the former, adaptive gamma correction to process the latter for generating various feature maps. In the end, these feature maps are united via adaptive multi‐scale fusion strategy to reconstruct high‐quality images, which are characterized by high contrast and brightness, vivid colour, and clearer details. Extensive experiments show that this method is a well‐proven low‐light image enhancement approach, which outperforms the state‐of‐the‐art comparison methods. Furthermore, the proposed method also can yield satisfying images in the heavy foggy, yellow sand, underwater, and other severe conditions.

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Adapted type-II fuzzy algorithm to process images with non-uniform illumination

Al-Ameen

2024

SIViP

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Reference-free low-light image enhancement by associating hierarchical wavelet representations

Cited by 7 publications

References 39 publications

Unsupervised Low-Light Image Enhancement via Virtual Diffraction Information in Frequency Domain

Unsupervised Low-Light Image Enhancement via Virtual Diffraction Information in Frequency Domain

Retinex‐inspired contrast stretch and detail boosting for lowlight image enhancement

Adapted type-II fuzzy algorithm to process images with non-uniform illumination

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