Fast Single Image Dehazing Using Saturation Based Transmission Map Estimation

Kim, Se Eun; Park, Tae Hee; Eom, Il Kyu

doi:10.1109/tip.2019.2948279

Cited by 70 publications

(64 citation statements)

References 50 publications

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“…However, since the proposed colour balance algorithm can achieve sufficient colour correction, the widely employed haze removal frameworks can be used without deformation. The colour corrected underwater images using the proposed method are applied to the conventional haze removal schemes, such as the dark channel prior (DCP) [10 ] (IUWB + DCP), and the saturation‐based dehazing (SBD) algorithm [11 ] (IUWB + SBD). The performance of the proposed method is compared with that of five state of the art methods: UWB‐based fusion (UWBF) algorithm [4 ], underwater haze line (UHL) [5 ], histogram distribution prior (HDP) [6 ], red channel prior (RCP) [7 ] and data‐ and prior‐aggregated transmission network (DPATN) [12 ].…”

Section: Resultsmentioning

confidence: 99%

“…However, DCP method can provide reasonable enhancement results if the balanced image based on the proposed colour correction is used as input (IUWB + DCP). Although SBD [11 ] method achieves good results on some images with yellowish underwater veil, it fails to improve reddish or bluish underwater images. On the other hand, IUWB + SBD is the most efficient one compared to the existing methods.…”

Section: Resultsmentioning

confidence: 99%

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Improvement of underwater colour correction using standard deviation ratio

2020

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This Letter presents a simple and new colour correction algorithm for the underwater image. The proposed method corrects the pixels of the weak colour channel based on the ratio of the standard deviation of the weak colour channel to that of the green channel. The corrected image based on the proposed colour balanced algorithm barely produces a reddish artefact and can be applied directly to the existing dehazing approaches without any processing. The experimental results show that the proposed algorithm achieves good colour correction performance, as well as reasonable underwater image enhancement when employed with the existing dehazing methods.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Improvement of underwater colour correction using standard deviation ratio

2020

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“…(21), assuming that K is the scale parameter, then the scale δv of v-axis can be expressed as Eq. (22).…”

Section: R K DC  mentioning

confidence: 99%

“…A lot of enhancement-based defogging algorithms are also researched by many scientists, for example, retinexbased algorithms [17][18][19][20], wavelet-based algorithms [21][22][23][24], etc. Hu [17] enhanced the fog image with enlarging the local contrast.…”

Section: Introductionmentioning

confidence: 99%

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An Anisotropic Gaussian Filtering Model for Image De-Hazing

Liu

Chen

et al. 2020

IEEE Access

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This paper proposes a de-hazing algorithm on the basis of the anisotropic Gaussian filtering method to overcome some essential limitations of the DCP-based (the dark channel prior, DCP) methods, such as halo artifacts and over-saturation problems. In this method, the approximate range of the global atmospheric light A is obtained by using the spatial LOG edge detection method, and the accurate A is acquired by combing binary algorithm. And an anisotropic Gaussian filtering method is adopted to optimize the transmission, which can smooth the rough transmission map, improve the clarity of image details and inhibit halo artifact and over-saturation effect. With the accurate A and optimized transmission, the dehazing results can be acquired on the basis of the atmospheric scattering model. The processing effect is verified by our own fog image data set constructed in real life or downloaded from the Internet. The visual effect of our method is more natural in brightness, contrast and detail recovery. Besides, the indicators SSIM, PSNR, Lum, Con, Inf, e and r are relatively high, and indicators MSE and ɛ are relatively low. Therefore, subjective and objective experimental results demonstrate that our method outperforms four state-of-the-art methods in terms of visual sense definition, robustness and time efficiency.

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An encoder‐decoder based CNN architecture using end to end dehaze and detection network for proper image visualization and detection

Shit

Das

Ray

et al. 2023

Computer Animation & Virtual

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Industrial sectors are reinventing in automation, stability, and robustness due to the rapid development of artificial intelligence technologies, resulting in significant increases in quality and production. Visual‐based sensor networks capture various views of the surrounding environment and are used to monitor industrial and transportation sectors. However, due to unclean suspended air particles that damage the whole monitoring and transportation systems, the visual quality of the images is degraded under adverse weather conditions. This research proposed a convolutional neural network‐based image dehazing and detection approach, called end to end dehaze and detection network (EDD‐N), for proper image visualization and detection. This network is trained on real‐time hazy images that are directly used to recover dehaze images without a transmission map. EDD‐N is robust, and accuracy is higher than any other proposed model. Finally, we conducted extensive experiments using real‐time foggy images. The quantitative and qualitative evaluations of the hazy dataset verify the proposed method's superiority over other dehazing methods. Moreover, the proposed method validated real‐time object detection tasks in adverse weather conditions and improved the intelligent transportation system.

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Fast Single Image Dehazing Using Saturation Based Transmission Map Estimation

Cited by 70 publications

References 50 publications

Improvement of underwater colour correction using standard deviation ratio

Improvement of underwater colour correction using standard deviation ratio

An Anisotropic Gaussian Filtering Model for Image De-Hazing

An encoder‐decoder based CNN architecture using end to end dehaze and detection network for proper image visualization and detection

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