This paper reviews and correlates various haze removal techniques to improve the atmospheric light estimation because the intensity of the dark channel is a rough approximation of the thickness of the haze. Images of outdoor scenes are usually degraded by the turbid medium (e.g., particles, water-droplets) in the atmosphere. Haze, fog, and smoke are such phenomena due to atmospheric absorption and scattering. The irradiance received by the camera from the scene point is attenuated along the line of sight. Furthermore, the incoming light is blended with the air light. The degraded images lose the contrast and color fidelity. In poor weather condition, fog removal from an image is an unavoidable problem. Various methods of fog removal have been described in literature. Initially, methods using multiple images which included the use of polarization filter. This method suffers from high time complexity and requirement of multiple images, resulting in non-suitability to real-time applications. This leads to the development of single image fog removal technique such as Dark Channel Prior (DCP), Improved Dark Channel Prior (IDCP), Dark channel prior with Histogram Specification, Anisotropic Diffusion and Improved Dark Channel Prior using Guided Filter. In proposed method, the dark channel is used to improve the atmospheric light estimation because the intensity of the dark channel is a rough approximation of the thickness of the haze. This property is used to estimate the transmission and the atmospheric light. A number of techniques has been proposed by various researchers to improve the atmospheric light estimation and to calculate air-light. The main intent of this review paper is to study and compare the distinct techniques of haze removal along with its drawbacks