Entropy based single image dehazing with refined transmission using holistic edges

Lakshmi, T. R. Vijaya; Reddy, Ch. Venkata Krishna; Padmavathi, K.; Swaraja, K.; Meenakshi, K.

doi:10.1007/s11042-022-12485-z

Cited by 6 publications

(2 citation statements)

References 42 publications

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“…The two primary causes of image degradation in foggy scenes are air-light scattering into stray light during propagation, which results in image blur, and absorption and scattering of the reflected light by water particles suspended in air, which reduce image brightness and contrast [ 20 , 21 , 22 , 23 ].…”

Section: Atmospheric Transmission Modelmentioning

confidence: 99%

Defogging Algorithm Based on Polarization Characteristics and Atmospheric Transmission Model

Zhang

Shi

et al. 2022

Sensors

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We propose a polarized image defogging algorithm according to the sky segmentation results and transmission map optimization. Firstly, we propose a joint sky segmentation method based on scene polarization information, gradient information and light intensity information. This method can effectively segment the sky region and accurately estimate the global parameters such as atmospheric polarization degree and atmospheric light intensity at infinite distance. Then, the Gaussian filter is used to solve the light intensity map of the target, and the information of the polarization degree of the target is solved. Finally, based on the segmented sky region, a three-step transmission optimization method is proposed, which can effectively suppress the halo effect in the reconstructed image of large area sky region. Experimental results shows that defogging has a big improvement in the average gradient of the image and the grayscale standard deviation. Therefore, the proposed algorithm provides strong defogging and can improve the optical imaging quality in foggy scenes by restoring fog-free images.

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Section: Atmospheric Transmission Modelmentioning

confidence: 99%

Defogging Algorithm Based on Polarization Characteristics and Atmospheric Transmission Model

Zhang

Shi

et al. 2022

Sensors

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“…There are numerous image processing methods designed to improve image quality by the removal of different kinds of distortions [ 1 , 2 , 3 , 4 , 5 , 6 ]. It is obvious that not every distortion can affect the entire image area; thus, every single pixel does not need to be processed.…”

Section: Introductionmentioning

confidence: 99%

Decomposed Dissimilarity Measure for Evaluation of Digital Image Denoising

Maliński

2023

Sensors

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A new approach to the evaluation of digital image denoising algorithms is presented. In the proposed method, the mean absolute error (MAE) is decomposed into three components that reflect the different cases of denoising imperfections. Moreover, aim plots are described, which are designed to be a very clear and intuitive form of presentation of the new decomposed measure. Finally, examples of the application of the decomposed MAE and the aim plots in the evaluation of impulsive noise removal algorithms are presented. The decomposed MAE measure is a hybrid of the image dissimilarity measure and detection performance measures. It provides information about sources of errors such as pixel estimation errors, unnecessary altered pixels, or undetected and uncorrected distorted pixels. It measures the impact of these factors on the overall correction performance. The decomposed MAE is suitable for the evaluation of algorithms that perform a detection of the distortion that affects only a certain fraction of the image pixels.

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