An improved dark channel defogging algorithm based on the HSI colour space

Cui, Yani; Zhi, Shuaiqing; Liu, Wenjin; Deng, Jiaxian; Ren, Jia

doi:10.1049/ipr2.12389

Cited by 9 publications

(3 citation statements)

References 29 publications

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“…( 1),   Ix represents the image containing fog to be processed, and A represents the pre estimated atmospheric light value. A converts the RGB coordinate system of pixels in foggy images to point A as the coordinate origin [18][19]. The joint image degradation model is shown in Eq.…”

Section: A Implementation Of Underwater Video Image Restoration On Th...mentioning

confidence: 99%

Underwater Video Image Restoration and Visual Communication Optimization Based on Improved Non Local Prior Algorithm

Xia

2024

IJACSA

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Underwater image processing should balance image clarity restoration and comprehensive display of underwater scenes, requiring image fusion and stitching techniques. The pixel level fusion method is based on pixels, and by fusing different image data, it eliminates stitching gaps and sudden changes in lighting intensity, preserves detailed information, and thus improves the accuracy of stitching images. In the process of restoring underwater video images without local priors, there is still room for optimization in steps such as removing atmospheric light values, estimating transmittance, and calculating dehazing images through regularization. Based on the characteristics of Jerlov water types, water quality is classified according to the properties of suspended solids, and each channel is adjusted to the compensation space to improve the restoration algorithm. Background light estimation is used to determine the degree of image degradation, select the optimal attenuation coefficient ratio, and restore the image. The experimental results show that it is crucial to choose a ratio of attenuation coefficients that is close to the actual water quality environment being photographed. Both this model and traditional algorithms have an accuracy rate of over 99.0%, with the accuracy of this model sometimes reaching 99.9%. Pixel level fusion and background light estimation technology optimize underwater images, improve stitching accuracy and clarity, enhance target detection and recognition, and have important value for marine exploration rigs.

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Section: A Implementation Of Underwater Video Image Restoration On Th...mentioning

confidence: 99%

Underwater Video Image Restoration and Visual Communication Optimization Based on Improved Non Local Prior Algorithm

Xia

2024

IJACSA

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“…The maximum inter-class variance method is used to convert each pixel of the grey-scale image to 0 or 255, reducing the number of image data and highlighting the target contours. (2) Gaussian blur (GB) [30]. Blur in an image means that the pixel value of the central pixel is the average of the sum of the pixel values of the surrounding pixels, as shown in Figure 6.…”

Section: Crack Geometry Measurement Algorithmmentioning

confidence: 99%

Research on a U-Net Bridge Crack Identification and Feature-Calculation Methods Based on a CBAM Attention Mechanism

Wang

Han

et al. 2022

Buildings

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Crack detection on bridges is an important part of assessing whether a bridge is safe for service. The methods using manual inspection and bridge-inspection vehicles have disadvantages, such as low efficiency and affecting road traffic. We have conducted an in-depth study of bridge-crack detection methods and have proposed a bridge crack identification algorithm for Unet, called the CBAM-Unet algorithm. CBAM (Convolutional Block Attention Module) is a lightweight convolutional attention module that combines a channel attention module (CAM) and a spatial attention module (SAM), which use an attention mechanism on a channel and spatially, respectively. CBAM takes into account the characteristics of bridge cracks. When the attention mechanism is used, the ability to express shallow feature information is enhanced, making the identified cracks more complete and accurate. Experimental results show that the algorithm can achieve an accuracy of 92.66% for crack identification. We used Gaussian fuzzy, Otsu and medial skeletonization algorithms to realise the post-processing of an image and obtain a medial skeleton map. A crack feature measurement algorithm based on the skeletonised image is proposed, which completes the measurement of the maximum width and length of the crack with errors of 1–6% and 1–8%, respectively, meeting the detection standard. The bridge crack feature extraction algorithm we present, CBAM-Unet, can effectively complete the crack-identification task, and the obtained image segmentation accuracy and parameter calculation meet the standards and requirements. This method greatly improves detection efficiency and accuracy, reduces detection costs and improves detection efficiency.

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“…As security surveillance becomes more widespread and computer vision rapidly advances, the demand for acquiring clear, highquality images is increasing 1,2 . Consequently, in the current field of digital image processing, image dehazing has gained extensive research attention as a critical method for enhancing image clarity 3,4 .…”

Section: Introductionmentioning

confidence: 99%

TMCD: Threshold multi-channel prior-based defogging algorithm for underground coal mine images

Fan,

Mao,

et al. 2024

Preprint

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With the rapid development of image dehazing algorithms, the demand for effective dehazing solutions across various industries has markedly increased. However, the application effectiveness of most existing image dehazing algorithms within coal mine environments remains suboptimal. Consequently, this paper proposes an image dehazing algorithm based on a threshold multi-channel inspection method. The algorithm detects fog density using an enhanced color attenuation prior method, followed by image enhancement in fog-free areas and dehazing in foggy areas through threshold multi-channel inspection. During fog density detection, the algorithm incorporates texture information and illumination invariance features from the HSV space, enhancing adaptability and robustness to different lighting conditions. In the dehazing process, segregating foggy and fog-free images facilitates more accurate and reliable dehazing outcomes. Moreover, by constructing a multi-scale pyramid and employing a guided filtering approach, the algorithm achieves more precise estimation of the image transmittance, mitigates the blocky artifacts common in traditional methods. For video dehazing, a parameter reuse mechanism based on inter-frame similarity is designed, improving the real-time performance of video dehazing. The algorithm was tested on a coal mine dataset and on partial public datasets such as NH-Haze2 and Dense-Haze, achieving experimental results that surpass other algorithms.

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An improved dark channel defogging algorithm based on the HSI colour space

Cited by 9 publications

References 29 publications

Underwater Video Image Restoration and Visual Communication Optimization Based on Improved Non Local Prior Algorithm

Underwater Video Image Restoration and Visual Communication Optimization Based on Improved Non Local Prior Algorithm

Research on a U-Net Bridge Crack Identification and Feature-Calculation Methods Based on a CBAM Attention Mechanism

TMCD: Threshold multi-channel prior-based defogging algorithm for underground coal mine images

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