Nighttime Haze Removal with Glow and Multiple Light Colors

Liu, Yu; Tan, Robby T.; Brown, Michael S.

doi:10.1109/iccv.2015.34

Cited by 189 publications

(193 citation statements)

References 20 publications

Supporting

Mentioning

189

Contrasting

Order By: Relevance

“…Figure 16 presents the results of a multiple degraded image, which includes color casts, low illumination and a hazy environment. We compare our results with He et al [23], Li et al [30], Lin et al [11] and Ancuti et al [32]. As can be seen, all of them can handle a part of the degradation.…”

Section: Underwater Enhancementsupporting

confidence: 54%

“…The Avg changes column shows the average changes in MSE and SSIM between the proposed method and those from the other studies. He's [23] and Ancuti's [32] approaches had a higher MSE and a lower SSIM than Li's [30] and Lin's [11] approaches; in other words, He's [23] and Ancuti's [32] approaches were less similar to the input image, as well as restored more details of the image. Our approach had the highest MSE and the lowest SSIM, meaning that the proposed method obtained more texture information than the other methods.…”

Section: Quantitative Comparisonmentioning

confidence: 99%

“…Combining the DCP with the guided filter [24] is also the most efficient method for the dehazing algorithm. Since then, the principal study of the fog algorithm has focused on the matting technique of transmittance [24][25][26][27][28][29][30]. Meng et al [25] applied a weighted L1-norm-based contextual regularization to optimize the estimation of the unknown scene transmission.…”

Section: Introductionmentioning

confidence: 99%

“…Zhang et al [28] used the five-dimensional feature vectors to recover the transmission values by finding their nearest neighbors from the fixed points. Li et al [30] computed a spatially varying atmospheric light map to predict the transmission and refined it by the guided filter [24]. The guided filter [24] is an O(n) time edge-preserving filter with the similar result of bilateral filter.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Enhancement of Low Contrast Images Based on Effective Space Combined with Pixel Learning

Han

2017

Information

View full text Add to dashboard Cite

Images captured in bad conditions often suffer from low contrast. In this paper, we proposed a simple, but efficient linear restoration model to enhance the low contrast images. The model's design is based on the effective space of the 3D surface graph of the image. Effective space is defined as the minimum space containing the 3D surface graph of the image, and the proportion of the pixel value in the effective space is considered to reflect the details of images. The bright channel prior and the dark channel prior are used to estimate the effective space, however, they may cause block artifacts. We designed the pixel learning to solve this problem. Pixel learning takes the input image as the training example and the low frequency component of input as the label to learn (pixel by pixel) based on the look-up table model. The proposed method is very fast and can restore a high-quality image with fine details. The experimental results on a variety of images captured in bad conditions, such as nonuniform light, night, hazy and underwater, demonstrate the effectiveness and efficiency of the proposed method.

show abstract

Section: Underwater Enhancementsupporting

confidence: 54%

Section: Quantitative Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Enhancement of Low Contrast Images Based on Effective Space Combined with Pixel Learning

Han

2017

Information

View full text Add to dashboard Cite

show abstract

“…Finally, the radiance image can be effectively restored by combining the MASM. Compared with the method proposed by Li et al, 7 Haze Removal based on Two Steps (HRTS) employs a new color constancy prior and an adaptive threshold, and the proposed model not only considers the effect of the ambient light, but also computes the transmission by improved guided filter by comparing the method with darkness channel prior. 6 Experimental results show that the proposed method could effectively remove the haze and obviously improve the color and visibility of the image.…”

mentioning

confidence: 99%

Single image haze removal based on two steps

Xiang

2017

Mod. Phys. Lett. B

View full text Add to dashboard Cite

In this paper, a novel method named Haze Removal based on Two Steps (HRTS) for removing the haze has been proposed based on two steps, which obviously improves the image qualities such as color and visibility caused by haze. The proposed method mainly consists of two steps: the preprocessing step by decomposing the input image to reduce the influence of ambient light and the removed haze step for restoring the radiance. We first reduce the effect of the ambient light by decomposing the haze image, estimate the transmission map based on the result of the decomposition, and then use the modified guided filter method to refine it. Finally, the monochrome atmospheric scattering model is combined to restore the radiance image. Experimental results show that the proposed method could effectively remove the haze and obviously improve the color and visibility of the image in the realistic scenes by comparing other existing haze removal methods.

show abstract

P‐3.20: A Novel Algorithm for Nighttime Image Dehazing

Jin

Wang

Yan

2022

Symp Digest of Tech Papers

View full text Add to dashboard Cite

In recent years, nighttime images collected under haze weather often have problems such as low contrast and serious texture loss. The previous algorithms still have some limitations for nighttime dehazing. In this paper, we proposed a novel nighttime dehazing algorithm and compare our results with existing daytime and nighttime dehazing algorithms� results. The experimental results show that the proposed algorithm has a better dehazing performance and shorter runtime. This algorithm can effectively address image distortion, texture loss and low brightness of the dehazing images.

show abstract

Nighttime Haze Removal with Glow and Multiple Light Colors

Cited by 189 publications

References 20 publications

Enhancement of Low Contrast Images Based on Effective Space Combined with Pixel Learning

Enhancement of Low Contrast Images Based on Effective Space Combined with Pixel Learning

Single image haze removal based on two steps

P‐3.20: A Novel Algorithm for Nighttime Image Dehazing

Contact Info

Product

Resources

About