Weighted Guided Gaussian Single Image Dehazing
| Autor: | B.K. Muhammed Nizar, Sajana M. Iqbal, Athira Abraham |
|---|---|
| Jazyk: | angličtina |
| Předmět: |
Channel (digital image)
Edge preserve smoothing Computer science Low-pass filter Edge aware weighting ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Edge-preserving smoothing 01 natural sciences Composite image filter 010309 optics symbols.namesake 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Gaussian Filter Computer vision Haze removal algorithms General Environmental Science business.industry Weighted Guided Filter Dark channel Filter (signal processing) Gaussian filter symbols General Earth and Planetary Sciences 020201 artificial intelligence & image processing Bilateral filter Artificial intelligence business Smoothing |
| Zdroj: | Procedia Technology. :293-301 |
| ISSN: | 2212-0173 |
| DOI: | 10.1016/j.protcy.2016.08.110 |
| Popis: | Remote sensing images such as satellite and underwater images are widely used in various fields of computer vision. But due to fog, mist and various aerosols in the atmosphere their contrast get reduced. So here proposing a simple and novel method to eliminate the haze on remote sensing images using two filters. Gaussian and Weighted guided filter is using in the method. This method is based on the dark channel prior and a common haze image model and two filters. In order to eliminate halo artifacts first, we use a low pass Gaussian filter. To refine the coarse estimated atmospheric veil, also we use this filter. We can redefine the transmission, for preventing the color distortion of the recovered images in the output; Gaussian filter is based on local optimized edge-preserving smoothing technique. But this filter suffers from halo artifacts and gradient reversal. So a weighted guided image filter (WGIF) is introduced by adding an edge aware weighting into an existing guided image filter (GIF) to increase the Naturalness and Sharpness along with visual clarity. The WGIF had advantages of both global and local smoothing filters.(1) the complexity of WGIF is O (N) for an image with N pixels which is same as the GIF used before 2) The WGIF can avoid halo artifacts like the existing global smoothing filters with increased visibility. With short increment on running times, it is effective for visually appealing remote sensing images. We will use the Guided image filtering algorithm and Box filter algorithm for dehazing the images. |
| Databáze: | OpenAIRE |
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