scholarly journals A Real-Time Haze Removal & Mosaicing using Rapid Prototype Hardware

2020 ◽  
Vol 9 (10) ◽  
pp. 29-34
Keyword(s):  
Real Time ◽  
Rapid Prototype ◽  
Field Of View ◽  
Corner Detection ◽  
Dark Channel Prior ◽  
Low Contrast ◽  
Whole Process ◽  
Dark Channel ◽  
Information Fidelity ◽  
Initial Focus

In this paper, a pivotal technique was proposed that reduces the haze and combines the haze free image to increase the Field of View (FoV) in real-time with a rapid prototype hardware device. The Initial focus is to reduce the haze in an image with Dark Channel Prior Technique and the FSD method is utilized to mosaic the haze free images. Low contrast may occur due to the scattering light, air particles or fog in nature which results in a haze image that needs to be reduced and enhance the image for better vicinity. Haze reduction approach depends on entropy and information fidelity. Our Haze free algorithm executes multiple phases such as dark channel prior computation, estimation and refinement of transmission map and restoration of RGB values. The second technique is the mosaic process that improves the field of view of a scene and the phases that execute are corner detection, extraction, geometric computation and blending. Our experimental results have shown better when compared to the other algorithms. The whole process is executed in real-time with a standalone device called Intel compute stick.

scholarly journals Real-Time Haze Removal Using Normalised Pixel-Wise Dark-Channel Prior and Robust Atmospheric-Light Estimation

2020 ◽  
Vol 10 (3) ◽  
pp. 1165 ◽  
Author(s):  
Yutaro Iwamoto ◽  
Naoaki Hashimoto ◽  
Yen-Wei Chen
Keyword(s):  
Real Time ◽  
Computational Cost ◽  
Dark Channel Prior ◽  
Detailed Structure ◽  
Haze Removal ◽  
Acceptable Quality ◽  
Colour Channel ◽  
Dark Channel ◽  
High Computational Cost ◽  
Atmospheric Light

This study proposes real-time haze removal from a single image using normalised pixel-wise dark-channel prior (DCP). DCP assumes that at least one RGB colour channel within most local patches in a haze-free image has a low-intensity value. Since the spatial resolution of the transmission map depends on the patch size and it loses the detailed structure with large patch sizes, original work refines the transmission map using an image-matting technique. However, it requires high computational cost and is not adequate for real-time application. To solve these problems, we use normalised pixel-wise haze estimation without losing the detailed structure of the transmission map. This study also proposes robust atmospheric-light estimation using a coarse-to-fine search strategy and down-sampled haze estimation for acceleration. Experiments with actual and simulated haze images showed that the proposed method achieves real-time results of visually and quantitatively acceptable quality compared with other conventional methods of haze removal.

scholarly journals Image Defogging Framework Using Segmentation and the Dark Channel Prior

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 285
Author(s):  
Sabiha Anan ◽  
Mohammad Ibrahim Khan ◽  
Mir Md Saki Kowsar ◽  
Kaushik Deb ◽  
Pranab Kumar Dhar ◽  
...  
Keyword(s):  
Histogram Equalization ◽  
Homogeneous Region ◽  
Experimental Result ◽  
Dark Channel Prior ◽  
Color Information ◽  
Visual Effects ◽  
Low Contrast ◽  
Adaptive Histogram Equalization ◽  
Processing Step ◽  
Dark Channel

Foggy images suffer from low contrast and poor visibility problem along with little color information of the scene. It is imperative to remove fog from images as a pre-processing step in computer vision. The Dark Channel Prior (DCP) technique is a very promising defogging technique due to excellent restoring results for images containing no homogeneous region. However, having a large homogeneous region such as sky region, the restored images suffer from color distortion and block effects. Thus, to overcome the limitation of DCP method, we introduce a framework which is based on sky and non-sky region segmentation and restoring sky and non-sky parts separately. Here, isolation of the sky and non-sky part is done by using a binary mask formulated by floodfill algorithm. The foggy sky part is restored by using Contrast Limited Adaptive Histogram Equalization (CLAHE) and non-sky part by modified DCP. The restored parts are blended together for the resultant image. The proposed method is evaluated using both synthetic and real world foggy images against state of the art techniques. The experimental result shows that our proposed method provides better entropy value than other stated techniques along with have better natural visual effects while consuming much lower processing time.

scholarly journals Underwater Image Enhancement Method Based On Color Correction and Dark Channel Prior

2021 ◽  
Vol 2066 (1) ◽  
pp. 012050
Author(s):  
Hao Chen ◽  
Hongsen He ◽  
Xinghua Feng
Keyword(s):  
Image Enhancement ◽  
Color Correction ◽  
Dark Channel Prior ◽  
Low Contrast ◽  
Underwater Environment ◽  
Underwater Image ◽  
Blue Channel ◽  
Dark Channel ◽  
Green Background ◽  
Enhancement Method

Abstract Concerning to the problem in the distortion of color and the low contrast of underwater image, the image enhancement method in the underwater environment based on color correction and dark channel prior was proposed. When dealing with the color bias problem, the blue channel standard ratio is firstly calculated based on the blue channel, and the red and green channels of the underwater image are compensated to remove the blue and green background colors of the underwater image. In light of the problem in the low contrast of image in underwater environment, the dark channel prior (DCP) method based on the super pixel was used to enhance the corrected underwater image. Finally, the underwater object detection dataset images are tested, and the algorithm proposed in terms of the quality is made the comparison with six advanced image enhancement method in underwater environment. The experimental results show that the proposed algorithm earned the highest score in underwater quality evaluation index (UIQM) compared with the above algorithm.

scholarly journals Real Time Night Vision Surveillance using Improved Dark Channel Prior

2021 ◽  
Vol 9 (VI) ◽  
pp. 1581-1590
Author(s):  
M. V. Naga Bhushanam
Keyword(s):  
Real Time ◽  
Cost Effective ◽  
Night Vision ◽  
Perceptual Quality ◽  
Dark Channel Prior ◽  
Video Enhancement ◽  
Maximum Information ◽  
Gaussian Pyramid ◽  
Dark Channel

Videos taken under low lighting conditions usually result in severe loss of visibility and contrast and are uncomfortable for observation and analysis. Night vision cameras that cater to the needs are expensive and less versatile. To be cost effective and extract maximum information from videos taken in low lit conditions, video enhancing techniques must be used. Though there are many night vision enhancement techniques available in literature, this paper particularly emphasizes about Improved Dark Channel Prior algorithm and its results. This approach suits well for real time night video enhancement. It has been found that a pixel-wise inversion of a night video appears very similar to the video obtained during foggy days. The same idea of haze removal approach is used to boost the visual quality of night videos. An improved dark channel prior model is presented that is integrated with Gaussian Pyramid operators for local smoothing. The experimental results show that the proposed method can boost the perceptual quality of detailing in night videos.

scholarly journals Real-time hardware accelerator for single image haze removal using dark channel prior and guided filter

2014 ◽  
Vol 11 (24) ◽  
pp. 20141002-20141002 ◽  
Author(s):  
Zhengfa Liang ◽  
Hengzhu Liu ◽  
Botao Zhang ◽  
Benzhang Wang
Keyword(s):  
Real Time ◽  
Guided Filter ◽  
Hardware Accelerator ◽  
Dark Channel Prior ◽  
Single Image ◽  
Haze Removal ◽  
Dark Channel
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