In real world scenario due to bad weather conditions the presence of fog and haze, the particles in the outdoor environment or atmosphere (e.g., droplets, smoke, sand, snow, mist, volcanic ash, liquid dust or solid dust) greatly reduces the visibility of the scene. As a consequence, the clarity of an image would be seriously degraded, which may decrease the performance of many image processing applications. Image Dehazing methods try to alleviate these problems by estimating a haze free version of the given hazy image. Traditionally the task of image dehazing can be processed as recovering the scene radiance from a noisy hazy image by estimating the atmospheric light and transmission properties. In those kinds of techniques, it additionally needs some more information regarding the image such as scene depth, weather condition parameters and so on. But this is not suitable for real world scenario. This research focus on proposing an approach to fully capture the intrinsic attributes of a hazy image and improves the performance of dehazing. Dark Channel Prior plays vital role in dehazing process. Hence this research focus on recovering dehaze version of the input image by CNN. So that all methods are comes under the categories image enhancement, image fusion image restoration based on statistical and structural features of the hazed image.
Gray-Scale Image Dehazing Guided by Scene Depth Information
