A Survey of Image Dehazing Algorithm Based on Retinex Theory

Wen, Haokang; Dai, Fengzhi; Wang, Dejin

doi:10.1109/iciibms50712.2020.9336197

Cited by 7 publications

(5 citation statements)

References 5 publications

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“…Enhancement algorithms based on wavelet transform 21 improve the temporal and frequency domain resolution of the preprocessed blurry image using wavelet transform. Subsequently, the Retinex algorithm [10][11][12] can be used to enhance color performance and improve color effects. However, image enhancement-based dehazing methods are prone to information loss, introduction of artifacts, and subjectivity in parameter selection.…”

Section: Image Processing-based Penetration Techniquesmentioning

confidence: 99%

“…However, in scenarios with high optical thickness of dense fog, the weak echo photon count from the target object, coupled with limitations in detector sensitivity and time resolution, makes it challenging to achieve efficient imaging of target scenes obstructed by dense fog. On the other hand, in the field of computer vision, algorithms [8][9][10][11][12][13][14] and machine learning methods [15][16][17][18] are commonly employed to reconstruct images obscured by fog, as captured by standard industrial cameras. By leveraging existing publicly available datasets, the learning of the underlying degradation process between occluded and ground truth images is facilitated.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Active non-line-of-sight imaging through dense fog via hybrid intelligent enhancement perception system

Xu,

Zhang,

Zhao

et al. 2024

Sixth Conference on Frontiers in Optical Imaging and Technology: Novel Imaging Systems

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Non-line-of-sight(NLOS) imaging through fog has been extensively researched in the fields of optics and computer vision. However, due to the influence of strong backscattering and diffuse reflection generated by the dense fog on the temporal-spatial correlations of photons returning from the target object, the reconstruction quality of most existing methods is significantly reduced under dense fog conditions. In this study, we define the optical imaging process in a foggy environment and propose a hybrid intelligent enhancement perception(HIEP) system based on Time-of-Flight(ToF) methods and physics-driven Swin transformer(ToFormer) to eliminate scattering effects and reconstruct targets under heterogeneous fog with varying optical thickness. Furthermore, we assembled a prototype of the HIEP system and established the Active Non-Line-of-Sight Imaging Through Dense Fog(NLOSTDF) dataset to train the reconstruction network. The experimental results demonstrate that even in dense fog short-distance scenarios with an optical thickness of up to 2.5 and imaging distances less than 6 meters, our approach achieves clear imaging of the target scene, surpassing existing optical and computer vision methods.

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Section: Image Processing-based Penetration Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Active non-line-of-sight imaging through dense fog via hybrid intelligent enhancement perception system

Xu,

Zhang,

Zhao

et al. 2024

Sixth Conference on Frontiers in Optical Imaging and Technology: Novel Imaging Systems

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show abstract

“…Researchers such as Jobson proposed the single-scale Retinex (SSR) algorithm [4], [5]. The specific formula is as follows: R i (x, y) = logI i (x, y) − log[I i (x, y) * F(x, y)] (6) In the formula, Ri(x,y) is the output of Retinex in the "i" color spectrum, Ii(x,y) is the image distribution, that is, the brightness value at the position (x,y).…”

Section: Retinex Algorithmmentioning

confidence: 99%

A Research on Image Defogging Algorithm Based on Image Enhancement

Wen

Sheng

et al. 2023

Proceedings of International Conference on Artificial Life and Robotics

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In order to solve the problem of low contrast image and loss of image details in the foggy weather, the image defogging technique is used to remove the noise in the image and improve the image contrast, so as to recover a clear and fog-free image. In this paper, we mainly introduce three image defogging algorithms: global histogram equalization, local histogram equalisation and the Retinex algorithm. The advantages and shortcomings of each algorithm are summarised through the study of the principles of each algorithm and the comparative analysis of the experimental result.

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“…One method proposed for brightening a lowilluminance area uniformly distributes the intensity values throughout the intensity histogram equalization of the image [18]. Research has also been conducted on improving the brightness value of low-light images by applying a retinex-based method [16,19]. To improve the brightness of the image, it is necessary to first determine whether the image contains a counterlight.…”

Section: Introductionmentioning

confidence: 99%

“…To overcome this, an adaptive threshold-based intensity equalization method [21] was proposed; however, it results in a blurring of the image edge information. In other studies, it is assumed that the intensity value of the original image is generated through a combination of the reflected light of an object and the illuminance caused by sunlight [16,19]. The retinex method assumes that the color and brightness values of the original image are composed of defects in the reflected light of an object and the illuminance value of sunlight.…”

Section: Introductionmentioning

confidence: 99%

Detection of Road Images Containing a Counterlight Using Multilevel Analysis

Kim

2021

Symmetry

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In this paper, a method for detecting real-time images that include counterlight produced by the sun, is proposed. It involves applying a multistep analysis of the size, location, and distribution of bright areas in the image. In general, images containing counterlight have a symmetrically high brightness value at a specific location spread over an extremely large region. In addition, the distribution and change in brightness in that specific region have a symmetrically large difference compared with other regions. Through a multistep analysis of these symmetrical features, it is determined whether counterlight is included in the image. The proposed method presents a processing time of approximately 0.7 s and a detection accuracy of 88%, suggesting that the approach can be applied to a safe driving support system for autonomous vehicles.

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A Survey of Image Dehazing Algorithm Based on Retinex Theory

Cited by 7 publications

References 5 publications

Active non-line-of-sight imaging through dense fog via hybrid intelligent enhancement perception system

Active non-line-of-sight imaging through dense fog via hybrid intelligent enhancement perception system

A Research on Image Defogging Algorithm Based on Image Enhancement

Detection of Road Images Containing a Counterlight Using Multilevel Analysis

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