Improved Dark Channel Prior for Image Defogging Using RGB and YCbCr Color Space

Tufail, Zahid; Khurshid, Khurram; Salman, Ahmad; Nizami, Imran Fareed

doi:10.1109/access.2018.2843261

Cited by 46 publications

(27 citation statements)

References 37 publications

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“…Although DCP demonstrated good dehazing performance and broad applicability (e.g., underwater image restoration [ 20 ]), it is computationally expensive due to soft matting use. This drawback left room for improvement and thereafter inspired researchers to several approaches [ 21 , 22 , 23 , 24 ]. He et al [ 25 ] also proposed a multi-function guided filter, which could replace soft matting to ease the burden of expensive computations at the cost of a certain degree of degradation in image quality.…”

Section: Preliminariesmentioning

confidence: 99%

Single-Image Visibility Restoration: A Machine Learning Approach and Its 4K-Capable Hardware Accelerator

Ngo

Lee

et al. 2020

Sensors

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In recent years, machine vision algorithms have played an influential role as core technologies in several practical applications, such as surveillance, autonomous driving, and object recognition/localization. However, as almost all such algorithms are applicable to clear weather conditions, their performance is severely affected by any atmospheric turbidity. Several image visibility restoration algorithms have been proposed to address this issue, and they have proven to be a highly efficient solution. This paper proposes a novel method to recover clear images from degraded ones. To this end, the proposed algorithm uses a supervised machine learning-based technique to estimate the pixel-wise extinction coefficients of the transmission medium and a novel compensation scheme to rectify the post-dehazing false enlargement of white objects. Also, a corresponding hardware accelerator implemented on a Field Programmable Gate Array chip is in order for facilitating real-time processing, a critical requirement of practical camera-based systems. Experimental results on both synthetic and real image datasets verified the proposed method’s superiority over existing benchmark approaches. Furthermore, the hardware synthesis results revealed that the accelerator exhibits a processing rate of nearly 271.67 Mpixel/s, enabling it to process 4K videos at 30.7 frames per second in real time.

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Section: Preliminariesmentioning

confidence: 99%

Single-Image Visibility Restoration: A Machine Learning Approach and Its 4K-Capable Hardware Accelerator

Ngo

Lee

et al. 2020

Sensors

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“…However, the contrast of infrared image is low and the texture details are fuzzy. The main reason is that the infrared ray will be affected by the atmospheric thermal radiation due to the distance between the target and the sensors in the scene is far away [4], [5]. In order to improve the contrast of infrared image and highlight the target details, image enhancement is an effective method [2], [4].…”

Section: Introductionmentioning

confidence: 99%

“…It can be seen that the enhanced infrared images have higher contrast, clearer texture detail and more comfortable visual effect. Thus, image enhancement has attracted more and more attention from researchers [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

“…The above methods are mainly based on the image histogram attributes and have achieved good enhancement effects. Therefore, histogram equalization has been widely applied in image defogging [5], medical image processing [26], [28] and target detection [12], [15]. Transform domain equalization is to transform the gray value of image to gradient domain or wavelet domain by gradient transform transformation or wavelet transformation for image enhancement [21], [22].…”

Section: Introductionmentioning

confidence: 99%

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An Adaptive Detail Equalization for Infrared Image Enhancement Based on Multi-Scale Convolution

Liu

Pan

2020

IEEE Access

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In order to solve the problem of low contrast and fuzzy detail in infrared image, we propose an infrared image enhancement method based on multi-scale and adaptive bi-interval histogram equalization with details. The whole image enhancement method mainly consists of four parts: details enhancement, contrast stretch, edge enhancement and reconstruction of enhancement images. Firstly, the multi-scale convolution is used to enhance the details of image; Secondly, taking maximize the variance between classes and minimize the variance as fitness function and solved the threshold of the infrared image by genetic algorithm, then dividing the infrared image into two sub-intervals according to the threshold. After that, the bi-interval histogram equalization with details is applied to enhance the global contrast, at the same time, using the mean square deviation and average gray equalization to improve the brightness of the image. Finally, the enhanced image by adaptive bi-interval histogram equalization with details and the image processed by adaptive limited Laplace operator are fused by linear weighting to reconstruct the final enhancement image. The experimental results show that the proposed method can outperform state-of-the-art ones in both qualitative and quantitative comparisons.

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“…They after that proposed a multi-function guided image filter [ 9 ], which could replace the soft-matting for accelerating the DCP at the cost of a specific degradation in image quality. The DCP has also been developed in many directions to overcome its shortcomings of slow processing rate and unpredictable performance in sky regions [ 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. A fast dehazing method proposed by Kim et al [ 15 ] and its corresponding 4K-capable intellectual property (IP) presented in Reference [ 17 ] are cases considered.…”

Section: Introductionmentioning

confidence: 99%

Single Image Haze Removal from Image Enhancement Perspective for Real-Time Vision-Based Systems

Ngo

Lee

Nguyen

et al. 2020

Sensors

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Vision-based systems operating outdoors are significantly affected by weather conditions, notably those related to atmospheric turbidity. Accordingly, haze removal algorithms, actively being researched over the last decade, have come into use as a pre-processing step. Although numerous approaches have existed previously, an efficient method coupled with fast implementation is still in great demand. This paper proposes a single image haze removal algorithm with a corresponding hardware implementation for facilitating real-time processing. Contrary to methods that invert the physical model describing the formation of hazy images, the proposed approach mainly exploits computationally efficient image processing techniques such as detail enhancement, multiple-exposure image fusion, and adaptive tone remapping. Therefore, it possesses low computational complexity while achieving good performance compared to other state-of-the-art methods. Moreover, the low computational cost also brings about a compact hardware implementation capable of handling high-quality videos at an acceptable rate, that is, greater than 25 frames per second, as verified with a Field Programmable Gate Array chip. The software source code and datasets are available online for public use.

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Improved Dark Channel Prior for Image Defogging Using RGB and YCbCr Color Space

Cited by 46 publications

References 37 publications

Single-Image Visibility Restoration: A Machine Learning Approach and Its 4K-Capable Hardware Accelerator

Single-Image Visibility Restoration: A Machine Learning Approach and Its 4K-Capable Hardware Accelerator

An Adaptive Detail Equalization for Infrared Image Enhancement Based on Multi-Scale Convolution

Single Image Haze Removal from Image Enhancement Perspective for Real-Time Vision-Based Systems

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