Object Identification from Dark/Blurred Image using WBWM and Gaussian Pyramid Techniques

Dehazing algorithm development has recently attracted many researchers. The dehazing algorithms are used in a variety of multimedia related research fields for image restoration, enhancement and segmentation. These researches have assisted us in correcting certain accidentally blurred photographs that were caused by motion blur distortion, out-of-focus objects, intense light, and physical flaws in camera lenses. In this paper, we aims to show the different dehazing techniques for the removal of haze in the images. This paper also discuss about the proposed prominent algorithm called White Balance Weight Map (WBWM) for dehazing. This algorithm is suitable for haze removal from images in both dark and light view. The white balance is used for enhancing the image contrast and the weight map is used for restoration of pixels for better view in hazed image. By using this algorithm we can visualize the objects clearly that present in the dehazed images. The main goal of this work is to give readers an intuitive grasp of the dehazing technique and white balance and weight map algorithm which have made tremendous progress in haze reduction. The effectiveness of the proposed algorithm is substantiated with the help of the image quality parameters calculated. Visual comparison of dehazed images and objective evaluation further validates the effectiveness of the proposed method.