Enhanced Single Image Uniform and Heterogeneous Fog Removal Using Guided Filter
In this chapter, we propose an effective method to remove uniform and heterogeneous fog from the image using dark channel prior (DCP) and guided filter. Variation in thickness of the fog present in variety of image has helped to analyze and upgrade the da
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Abstract In this chapter, we propose an effective method to remove uniform and heterogeneous fog from the image using dark channel prior (DCP) and guided filter. Variation in thickness of the fog present in variety of image has helped to analyze and upgrade the dark channel prior image quality. Fog acts as veil which obscures the original scene radiance. A significant amount of fog could be observed around the edges of the objects in fog free image which is carried from the foggy image during intermediate processing steps. Efficient selection of dark channel prior kernel parameters helps us to carry minimum fog from the input foggy image to fog free image which has certainly reduced the halo effect around the edges of the objects in the fog free image. Also the use of guided filter to preserve edges of the objects in image is a fast and cost effective approach toward generation of fog less image.
Keywords Digital image processing Fog removal Image enhancement Digital filter
1 Introduction Today, the increasing demand of minute but significant real-time data and its analysis has given birth to too many fields of study like artificial intelligence, computer vision which is contributing toward mankind with its industrial application and feasibility. ADAS (Advance driving assistance system), being one of its application has reached us at a very fast rate and vividly seen in most of the countries where there
Pallawi (&) V. Natarajan Embedded System Technology, Electronics and Communication Engineering, SRM University, Chennai 603203, India e-mail: [email protected] V. Natarajan e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2017 S.S. Dash et al. (eds.), Artificial Intelligence and Evolutionary Computations in Engineering Systems, Advances in Intelligent Systems and Computing 517, DOI 10.1007/978-981-10-3174-8_38
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are well-maintained road infrastructures to deliver smooth running of computer vision and image processing algorithms. A very important point to notice here is that performances of vision algorithms (e.g., feature detection, filtering, and sign recognition) are dependent on the inputs given to the sensors like camera or radar in form of images or signals are actively contributed by quality of road infrastructures like lane marking and their feature, milestones, speed limit posts, and many others. Unfortunately, due to poor visibility conditions the color fidelity of the input images are lost reason being presence of fog, haze, dust, or smoke in the atmosphere. This creates turmoil in the decision making abilities and sometimes a complete failure of system which might lead to catastrophic events too. Fog and haze deters the scene radiance and gives us insignificant and sometimes no information about the scene of interest. Thus fog removal is highly desired to increases the visibility of the scene and correct the color shifts caused by airlight. Fog removal can also produce depth information and benefit many vision algorithms. Therefore, many algorithm
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