Image Haze Removal Algorithm Using a Logarithmic Guide Filtering and Multi-Channel Prior

Zou, Yuanyang; Ma, Yingshuang; Zhuang, Liyun; Wang, Guoxin

doi:10.1109/access.2021.3050260

Cited by 8 publications

(3 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It can restore the haze free image and reduce the color distortion of the bright area. Zou et al [ 7 ] proposed a new defogging algorithm for blurred images. First, the logarithmic pilot filter was used to estimate the ambient light, which retained the characteristics of the image in the bright light source region and improved the fuzzi-ness of the dark light source region.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Research on Haze Image Enhancement based on Dark Channel Prior Algorithm in Machine Vision

Sun

Wang

et al. 2022

Journal of Environmental and Public Health

View full text Add to dashboard Cite

According to the characteristics of foggy images, such as high noise, low resolution, and uneven illumination, an improved foggy image enhancement method based on dark channel priority is proposed. First, the new algorithm refines the transmittance and optimizes the atmospheric light value and converts the restored image to HSV space. Second, the brightness V component is enhanced by MSRCR algorithm improved by bilateral filtering, and the saturation S is improved by adaptive stretching algorithm. Finally, the image is converted from HSV space to RGB space to complete image enhancement. The new method solves the problems of that the color of large area is uneven and the overall color of the image is dark when the traditional dark channel prior method is used to remove fog. The experimental results show that from subjective evaluation and quantitative analysis the new algorithm overcomes the shortcomings of noise amplification and edge blur when the conventional enhancement algorithm enhances the image. It can improve image darkening and avoid image distortion in JPEG, BMP, GIF, PNG, PSD, and TIFF formats. By comparing with other image enhancement algorithms, the improved algorithm performs better than DCP, SSR, MSR, MSRCR, and CLAHE algorithm in PSNR, SSIM, and IE evaluation indexes. It has a good effect on preserving the edge information and has good adaptability and stability for heavily polluted haze image enhancement.

show abstract

Section: Introductionmentioning

confidence: 99%

[Retracted] Research on Haze Image Enhancement based on Dark Channel Prior Algorithm in Machine Vision

Sun

Wang

et al. 2022

Journal of Environmental and Public Health

View full text Add to dashboard Cite

show abstract

“…To prevent this situation, the minimum threshold of transmittance is set, expressed as t 0 = 0.1. Guided filtering is applied to further optimize transmittance [27][28][29]. The formula for obtaining the final clear image is as follows:…”

Section: Improved Dark Channel Prior Approachmentioning

confidence: 99%

Rethinking Underwater Crab Detection via Defogging and Channel Compensation

Sun,

Yuan,

et al. 2024

Fishes

View full text Add to dashboard Cite

Crab aquaculture is an important component of the freshwater aquaculture industry in China, encompassing an expansive farming area of over 6000 km2 nationwide. Currently, crab farmers rely on manually monitored feeding platforms to count the number and assess the distribution of crabs in the pond. However, this method is inefficient and lacks automation. To address the problem of efficient and rapid detection of crabs via automated systems based on machine vision in low-brightness underwater environments, a two-step color correction and improved dark channel prior underwater image processing approach for crab detection is proposed in this paper. Firstly, the parameters of the dark channel prior are optimized with guided filtering and quadtrees to solve the problems of blurred underwater images and artificial lighting. Then, the gray world assumption, the perfect reflection assumption, and a strong channel to compensate for the weak channel are applied to improve the pixels of red and blue channels, correct the color of the defogged image, optimize the visual effect of the image, and enrich the image information. Finally, ShuffleNetV2 is applied to optimize the target detection model to improve the model detection speed and real-time performance. The experimental results show that the proposed method has a detection rate of 90.78% and an average confidence level of 0.75. Compared with the improved YOLOv5s detection results of the original image, the detection rate of the proposed method is increased by 21.41%, and the average confidence level is increased by 47.06%, which meets a good standard. This approach could effectively build an underwater crab distribution map and provide scientific guidance for crab farming.

show abstract

“…Also, the transmission map can cause problems such as false textures and blocking artifacts and decrease the dark channel's apparent resolution due to the local patch [24]. Therefore the transmission map needs refining subsequently by using a filter such as Gaussian filter [42]- [44], bilateral filter [11], [45], soft matting [6], cross-bilateral filter [46], [47], adaptive filter [48], second-generation wavelet filter [16], or guided filter [43], [49]- [51]. However, it increases computational complexity and suffers from other artifacts [9].…”

Section: Introductionmentioning

confidence: 99%

MCPA: A Fast Single Image Haze Removal Method Based on the Minimum Channel and Patchless Approach

Fuh

Tung

2022

IEEE Access

View full text Add to dashboard Cite

The image haze removal algorithm is challenging regarding computational processing speed and the hazy removal effect. Instead of using the local patch approach, which assumes the scene transmission to be locally constant and uses various filters to smooth the transmission map, this paper proposes a fast single image haze removal method based on a minimum channel and patchless approach. A new simple approach to estimate the atmospheric light and the scene transmission is proposed based on the minimum channel of images. The histogram of the minimum channel of the image is used to extract the atmospheric light pixels and exclude the non-hazy bright pixels in the image. The histogram equalization and image multiplication are applied to achieve better visual quality. In order to verify the performance of the proposed method, 100 images are collected from datasets I-HAZE, O-HAZE, and websites. Experimental results show that our proposed method outperforms up-to-date state-of-the-art haze removal algorithms using quantitative evaluations. From subjective comparisons, the proposed method outperforms most current haze removal algorithms in color restoration. Also, time assessment results show that our proposed method is the fastest among the up-to-date state-of-the-art haze removal methods and is about 15 times faster than the secondfastest method. The main contribution of the proposed method is significantly reducing computation time because it uses a patchless approach that does not need any filter and complicated algorithms. In addition to significantly reducing the computational processing speed, our proposed method can achieve better visual quality.

show abstract

Image Haze Removal Algorithm Using a Logarithmic Guide Filtering and Multi-Channel Prior

Cited by 8 publications

References 67 publications

[Retracted] Research on Haze Image Enhancement based on Dark Channel Prior Algorithm in Machine Vision

[Retracted] Research on Haze Image Enhancement based on Dark Channel Prior Algorithm in Machine Vision

Rethinking Underwater Crab Detection via Defogging and Channel Compensation

MCPA: A Fast Single Image Haze Removal Method Based on the Minimum Channel and Patchless Approach

Contact Info

Product

Resources

About