| dc.description.abstract |
The immersive growth of multimedia sensors in conjunction with IoT in smart cities has increased the requirement of efficient multimedia computing. One such example is the degradation of images during bad weather and their efficient processing to help surveillance, driving assistance, etc. Haze removal is an important procedure to avoid ill-condition visibility of the captured images. An optimized haze removal technique helps the local administrative authorities by integrating multimedia sensors with the IoT to improve the quality of life. In this chapter, we analyze the well-known single image dehazing technique – Dark Channel Prior (DCP) in terms of getting better image quality and optimized computational time. The DCP method is stable and a benchmark for dehazing. However, we found that the haze removal effects obtained by the DCP can be further enhanced by preprocessing the underlying image for contrast enhancement. Also, the computational time is improved with respect to the underlying refinements. Considering the dehazing quality and computational efficiency, we analyze the image quality and demonstrate that it can be further improved with a better preprocessing mechanism. The experiments are conducted over a wide variety of haze images from standard datasets and sufficient improvements over the original DCP method are demonstrated. |
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