Contrast-based stereoscopic images dehazing
Yimin Qiu, Shiqian Wu · 2015
As human eyes perceive scenes with slightly different angles, fog effect is referred to the function of the distance between camera and objects. In this paper, a novel contrast-based dehazing algorithm is proposed by using stereoscopic images. The proposed algorithm first decomposes the disparity map in a given fog-and-haze stereo pair with digital wavelet transformation (DWT). Then, the contrast sensitivity function (CSF) is employed to adjust the image contrast. To measure the contrast, the cost function, which consists of the DWT CSF mask and wavelet contrast measurement is proposed. Results on a variety of real hazy images demonstrate that the proposed approach significantly improves hazy image quality. Especially, the proposed method has fast speed so that it can be implemented in real-time applications.