Image dehazing based on dark channel spatial stimuli gradient model and image morphology

Abstract

Image dehazing has become a critical problem to cater to as it has several parameters that need to be addressed. Real color, contrast, and illumination are the major parameters that are to be restored in the dehazed image. Different scenarios distort these parameters in different ways so it is difficult to restore the original image. Many approaches are used in the literature to cater to these problems but suffer from low contrast, faded color, and weak edges. This article introduces an effective technique, which is named as dark channel spatial stimuli gradient model (DCSSGM) that performs well for the aforementioned problems. The DCSSGM technique applies the dark channel prior (DCP) and spatial stimuli gradient sketch model (SSGSM) on each color channel to eliminate the haze from the image and to restore true edges. SSGSM is responsible to restore robust edges in an image using the perceived brightness and calculations based on neighborhood similarity. Morphology is applied to the resultant image to receive sharp true edges. The final restored image is the output of dynamic histogram equalization (DHE) which restores the contrast of the image. The evaluation analysis qualitatively and quantitatively concludes that the DCSSGM technique outperforms other state-of-the-art image dehazing techniques.