Haze Removal: An Approach Based on Saturation Component

Gadnayak, Khitish Kumar; Panda, Pankajini; Panda, Niranjan

doi:10.1007/978-81-322-2009-1_33

Cited by 2 publications

(6 citation statements)

References 5 publications

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“…The biggest feature of this image is the blurring of the scene due to decrease of visible distance and loss of high frequency component [5][6][7][8][9]. In addition, fog makes the color of the object similar to the fog in distance, and makes it difficult to distinguish the shape of the object by decreasing the saturation [4][5]. Therefore, early fog removal methods simply use the method of making the distinction between the background and the object clear by improving the contrast of the image.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, many researchers have proposed various methods using multiple images or using external devices. These research methods can be roughly divided into two areas [3][4]. The first is based on contrast enhancement and the second is based on image quality restoration.…”

Section: Related Workmentioning

confidence: 99%

“…The first is based on contrast enhancement and the second is based on image quality restoration. A representative method based on contrast enhancement is histogram smoothing [1][2][3][4][5]. In this method, color fidelity is not always maintained and a method of maximizing the contrast of the image is performed.…”

Section: Related Workmentioning

confidence: 99%

“…These additional information is the distance of the scene, the correlation between shadow and transmission, empirical assumptions, and the nature of the appropriate scene. The methods based on the initial image restoration use various additional information [4][5]. The effect of fog depends on the distance between the object and the camera [4][5].…”

Section: Related Workmentioning

confidence: 99%

“…Recently, a smart car system has installed a camera in a vehicle to detect collision or danger, and has evolved into an intelligent system that combines various vision technologies with a simple black-box system of a conventional recording system. However, there are environmental factors due to weather as the biggest obstacle to the development of this system [3][4][5]. In particular, fog causes scattering and attenuation of light to make the colors of surrounding areas all alike, making the color saturation very difficult, making it difficult to distinguish the shape of objects.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Research of improved object detection with image dehazing

Ahn¹,

Lee²,

Cho³

2018

IJET

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Background/Objectives: In this paper, we propose an improved object recognition method that can recover visibility by detecting degraded image due to factors such as weather, and to detect objects in the reconstructed image.Methods/Statistical analysis: In this paper, we propose improved object detection and recognition method by visibility restoration by improving degraded image. First, fog removal based on a single image is performed and restored using a median filter instead of using the minimum value in the window. We also used adaptive feature point extraction method to extract feature points by removing unnecessary information such as noise from the improved image.Findings: The performance of the proposed algorithm was evaluated by measuring the number of feature points before and after the proposed algorithm. Experimental results show that more feature points are extracted and matched than the case of recognition of the region of interest after reconstructing the image by removing the fog using the intermediate value filter in the image of degraded image by the proposed method.Improvements/Applications: Applying the proposed method to a variety of vision - based intelligent systems such as smart cars will eliminate the bad conditions such as fog, which will give better performance.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Research of improved object detection with image dehazing

Ahn¹,

Lee²,

Cho³

2018

IJET

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Improved Haze Removal of Underwater Images using Particle Swarm Optimization

Sharma¹,

Bhalla²

2015

IJCA

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The main objective of fog removal algorithm is to estimate the airlight map for the given image and then perform the necessary operations on the image in order to overcome the fog in the image and enhance the quality of the image. The dark channel prior method of fog removal is more suitable and time-saving in real-time systems. In this paper, an efficient approach for fog removal of foggy images based on the combination of dark channel prior and genetic algorithm is presented. It is found that the proposed method is more suitable for obtaining the better quality of the image than the most of the existing methods.

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Haze Removal: An Approach Based on Saturation Component

Cited by 2 publications

References 5 publications

Research of improved object detection with image dehazing

Research of improved object detection with image dehazing

Improved Haze Removal of Underwater Images using Particle Swarm Optimization

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