An Improved Image Dehazing Technique using CLAHE and Guided Filter

Soni, Badal; Mathur, Prachi

doi:10.1109/spin48934.2020.9071296

Cited by 20 publications

(8 citation statements)

References 25 publications

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“…The comparison results of our haze removal algorithm with MSRCR [48] and CLAHE [49] are show in Table Ⅰ. It shows our algorithm achieves higher IE, SSIM and PSNR compared to other algorithms.…”

Section: B Dehazing Experimentsmentioning

confidence: 92%

An End-to-End Dehazing Siamese Region Proposal Network for High Robustness Object Tracking

et al. 2021

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The haze scenes will bring negative impact to the object tracking process. In haze scenes, aerosols in the air will decrease the information entropy of the image generated by the imaging device. The reduction of information entropy means the loss of image detail information, which will mislead the existing algorithms to extract wrong object feature, causing the failure of tracking. Firstly, this paper explores the problem and proposes an end-to-end Dehazing Siamese Region Proposal Network (DH-SiamRPN). Then, we innovatively design an adaptive transmittance estimation module based on image quality evaluation index, and pre-integrate it into the dark channel dehazing algorithm to preprocess the video sequence. After that, based on the siamese network framework, AlexNet is employed to extract the depth features of the image pair, and the cross-correlation operation of classification and regression is performed according to the regional proposal network (RPN). In addition, inspired by DIoU modeling ideas, we innovatively optimize the proposal selection part. The experimental results demonstrate that the DH-SiamRPN has obvious advantages in precision and speed compared with other state-of-the-art trackers. Its tracking performance meets the requirements of high robustness and speed.

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Section: B Dehazing Experimentsmentioning

confidence: 92%

An End-to-End Dehazing Siamese Region Proposal Network for High Robustness Object Tracking

et al. 2021

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“…They are well-known performance metrics for assessing the degree of inaccuracy [4]. In order to assess the quality of the hazy/foggy images, measures like MSE and PSNR are frequently used [29].…”

Section: Performance Evaluation Methodsmentioning

confidence: 99%

Digital image enhancement by brightness and contrast manipulation using Verilog hardware description language

Azhari

Setumin

Noorsal

2023

IJECE

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A foggy environment may cause digitally captured images to appear blurry, dim, or low in contrast. This will impact computer vision systems that rely on image information. With the need for real-time image information, such as a plate number recognition system, a simple yet effective image enhancement algorithm using a hardware implementation is very much needed to fulfil the need. To improve images that suffer from low exposure and hazy, the hardware implementations are usually based on complex algorithms. Hence, the aim of this paper is to propose a less complex enhancement algorithm for hardware implementation that is able to improve the quality of such images. The proposed method simply combines brightness and contrast manipulation to enhance the image. In order to see the performance of the proposed method, a total of 100 vehicle registration number images were collected, enhanced, and evaluated. The evaluation results were compared to two other enhancement methods quantitatively and qualitatively. Quantitative evaluation is done by evaluating the output image using peak signal-to-noise ratio and mean-square error evaluation metrics, while a survey is done to evaluate the output image qualitatively. Based on the quantitative evaluation results, our proposed method outperforms the other two enhancement methods.

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“…The hazy-free image obtained by Equation (1), such as algorithms DCP, CAP, etc. ; the non-physical model defogging algorithm uses image enhancement methods to dehaze, such as Retinex defogging algorithm, 9 histogram equalization, 10 wavelet and homomorphic filtering algorithms. At the same time, the learning-based dehazing algorithm can also be similarly divided into two parts, the estimated parameter method and the direct restoration method: the estimated parameter method is to estimate 𝑡(𝑥) and 𝐴 through network learning to perform defogging, and the parameters estimated by using deep learning are generally more accurate than the traditional ones, such as MSCNN, 5 DehazeNet 4 and DCPDN; 6 the direct repair method is to directly learn and estimate the output dehaze image from the input fog image through the network, such as FD-GAN, 11 GridDehazeNet 12 and FFA-Net.…”

Section: Related Workmentioning

confidence: 99%

A high-resolution image dehazing GAN model in icing meteorological environment

Yang,

Zhou,

Zuo

et al. 2023

International Conference on Images, Signals, and Computing (ICISC 2023)

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In this paper, we propose a high-resolution GAN model for image dehazing in icing meteorological environment, which strictly follows a physics-driven scattering strategy. First of all, the utilization of sub-pixel convolution realizes the model to remove image artifacts and generate high-resolution images. Secondly, we use Patch-GAN in the discriminator to drive the generator to generate a haze-free image by capturing the details and local information of the image. Furthermore, to restore the texture information of the hazy image and reduce color distortion, the model is jointly trained by multiple loss functions. Experiments show the proposed method achieves advanced performance for image dehazing in icing weather environment.

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An Improved Image Dehazing Technique using CLAHE and Guided Filter

Cited by 20 publications

References 25 publications

An End-to-End Dehazing Siamese Region Proposal Network for High Robustness Object Tracking

An End-to-End Dehazing Siamese Region Proposal Network for High Robustness Object Tracking

Digital image enhancement by brightness and contrast manipulation using Verilog hardware description language

A high-resolution image dehazing GAN model in icing meteorological environment

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