Real-time defogging hardware accelerator based on improved dark channel prior and adaptive guided filtering

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.

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

Applied Sciences ◽

10.3390/app10031165 ◽

2020 ◽

Vol 10 (3) ◽

pp. 1165 ◽

Cited By ~ 2

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.

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Real-time single image dehazing using block-to-pixel interpolation and adaptive dark channel prior

IET Image Processing ◽

10.1049/iet-ipr.2015.0087 ◽

2015 ◽

Vol 9 (9) ◽

pp. 725-734 ◽

Cited By ~ 33

Author(s):

Teng Yu ◽

Irfan Riaz ◽

Hyunchul Shin ◽

Jingchun Piao

Keyword(s):

Real Time ◽

Dark Channel Prior ◽

Single Image ◽

Image Dehazing ◽

Dark Channel ◽

Pixel Interpolation ◽

Single Image Dehazing

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Real Time Night Vision Surveillance using Improved Dark Channel Prior

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.35279 ◽

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.

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