Color image dehazing using surround filter and dark channel prior

Nair, Deepa; Sankaran, Praveen

doi:10.1016/j.jvcir.2017.11.005

Cited by 46 publications

(14 citation statements)

References 27 publications

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“…Nair and Sankaran [14] implemented a haze removal approach using a dark channel prior and surround filter. e computational complexity of the approach is due to simple convolution.…”

Section: Related Workmentioning

confidence: 99%

“…e depth map and atmospheric veil were estimated to remove the visibility degradation from weather degraded images. It discovers the law of weather degraded image formation by considering the visual manifestations under various environmental circumstances [14]. Due to the extensive computational complexity of the physical model, He et al [15] implemented a novel channel prior, that is, dark channel prior (DCP).…”

Section: Introductionmentioning

confidence: 99%

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Efficient Single Image Dehazing Model Using Metaheuristics-Based Brightness Channel Prior

Kehar

Chopra²,

Singh

et al. 2021

Mathematical Problems in Engineering

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Haze degrades the spatial and spectral information of outdoor images. It may reduce the performance of the existing imaging models. Therefore, various visibility restoration models approaches have been designed to restore haze from still images. But restoring the haze is an open area of research. Although the existing approaches perform significantly better, they are not so effective against a large haze gradient. Also, the effect of hyperparameters tuning issue is also ignored. Therefore, a brightness channel prior (BCP) based dehazing model is proposed. The gradient filter is utilized to improve the transmission map computed using the gradient filter. Nondominated Sorting Genetic Algorithm is also used to optimize the initial parameters of the BCP approach. The comparative analysis shows that BCP performs effectively across a wide range of haze degradation levels without causing any visible artifacts.

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“…Nair and Sankaran [14] implemented a haze removal approach using a dark channel prior and surround filter. e computational complexity of the approach is due to simple convolution.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficient Single Image Dehazing Model Using Metaheuristics-Based Brightness Channel Prior

Kehar

Chopra²,

Singh

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Subsequently, Yano and Kuroki approximated the 2D Gaussian filter using multilayer convolution of multiple binomial filters enabled with basic shift and add operations for its faster implementation [23], while the Gaussian kernel of an edge preserving bilateral filter was approximated using raised cosines and MonteCarlo sampling takes around 17 s on a Intel 4-core machine for an image size of 512 × 512 pixels [24]. Nair and Sankaran presented a center surround filter to reduce the speed and memory requirement for color image dehazing in RGB, Lab and HSV color spaces, but its computation cost is still high for a small scaled image [25]. Preeti and vishvaksenan [26] have compared CPU and GPU implementation of Gaussian filter using OpenCV library packages.…”

Section: Related Workmentioning

confidence: 99%

Optimized OpenCL™ kernels for frequency domain image high-boost filters using image vectorization technique

Satapathy

Livingston

2019

SN Appl. Sci.

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Image high boost filtering uses high-boost filters to enhance the quality of an image, which has also seen in remote sensing, satellite broadcasting, classroom monitoring, and many more real-time video processing applications and requires its faster implementation. OpenCL is a widely adapted parallel programming framework that provides core level data parallelism, and dedicated for heterogeneous parallel devices like from low cost DSP to high-end CPU, GPU and FPGA. In this article, we have considered mostly used Ideal, Gaussian, Butterworth, and Laplacian of Gaussian frequency domain high-boost filters and implemented channelized OpenCL kernels for their rapid execution. In addition to that, these kernels are modified using image vectorization technique to optimize their time utilization by reducing the execution time of these OpenCL kernels to half. At last, performance analysis is carried out for these two types of OpenCL kernel implementations to determine their effectiveness with regard to time consumption and accuracy. Here, different image performance evaluation metrics like entropy, standard deviation, mean absolute error, percentage fit error, SSIM, correlation, and peak signal to noise ratio are applied to measure rightness of the above high-boost filters. From the results, we have concluded that a vectorized Butterworth high-boost filter kernel is the suitable one to provide better results among those filters, which might be highly adaptable in time bound real-time applications using various embedded devices.

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“…Binary tree-based atmospheric light estimation is proposed by Tang et al in [14]. Image defogging using RGB, LAB, and HSV color spaces is proposed by Nair et al [15]. In this approach, transmission map is refined using surround filter and the surround constant is chosen arbitrarily.…”

Section: Introductionmentioning

confidence: 99%

Segmentation-based image defogging using modified dark channel prior

Sabir

Khurshid

Salman

2020

J Image Video Proc.

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Image acquisition under bad weather conditions is prone to yield image with low contrast, faded color, and overall poor visibility. Different computer vision applications including surveillance, object classification, tracking, and recognition get effected due to degraded hazy images. Dehazing can significantly improve contrast, balance luminance, correct distortion, remove unwanted visual effects/ and therefore enhance the image quality. As a result, image defogging is imperative pre-processing step in computer vision applications. Previously, dark channel prior-based algorithms have proven promising results over the available techniques. In this paper, we have proposed a modified dark channel prior that uses fog density and guided image-filtering technique to estimate and refine transmission map, respectively. Guided image filter speeds up the refinement of transmission map, hence reduces the overall computational complexity of algorithm. We have also incorporated segmentation of the foggy image into sky and non-sky regions, after which, the modified dark channel prior and atmospheric light is computed for each segment. Then, the average value of atmospheric light for each segment is used to estimate transmission map. We have performed quantitative and subjective comparison for effective evaluation of our proposed algorithm against the current state-of-the-art algorithms on natural and synthetic images. Different quality metrics, such as saturation, mean square error, fog density, peak signal to noise ratio, structural similarity index metric, dehazing algorithm index (DHQI), full-reference image quality assessmen (FR-IQA), and naturalness of dehazed images have shown the proposed algorithm to be better than existing techniques.

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Color image dehazing using surround filter and dark channel prior

Cited by 46 publications

References 27 publications

Efficient Single Image Dehazing Model Using Metaheuristics-Based Brightness Channel Prior

Efficient Single Image Dehazing Model Using Metaheuristics-Based Brightness Channel Prior

Optimized OpenCL™ kernels for frequency domain image high-boost filters using image vectorization technique

Segmentation-based image defogging using modified dark channel prior

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