Text aware image dehazing using stroke width transform
Haze removal, which is also referred to as image dehazing, has been extensively used to improve the visibility in images captured under inclement weather. In particular, the dark channel prior (DCP) based single image dehazing has received the greatest amount of interest due to its superior performance. However, since the DCP is based on the characteristics of natural outdoor images, its reliability tends to decrease especially when an image contains man made textures. In this paper, we present a DCP based single image dehazing method that is robust when text or text like patterns are present in the image. The proposed method first estimates the text likelihood from a hazy image using the stroke width transform (SWT) and uses the estimated likelihood to correct the DCP. The experimental results show that the proposed algorithm outperforms the conventional DCP based dehazing methods.
2016 IEEE International Conference on Image Processing (ICIP)