Capturing the image in fog scene suffers from the distortions of the information and the time taken to predict the object in the way become complicated. To overcome this difficulty, the images are taken into consideration as new a technology opens door for converting the analog scene to digital scene in the form of an image. The image captured in less visibility of the scene predominantly in foggy weather conditions, the structure of image, and also several human activities like drones, aircrafts, flights, and travellers affect several computer vision applications like tracking, artificial intelligence, remote sensing, etc. Thus restoring back the outdoor scene from such foggy image is significantly important. The main focus is to defog the image in the patch; atmospheric light in foggy days looks as that of the fog, so this to be reduced; edges and corners must be visible, inner depths to be reconstructed from the fog scene. To fulfill these characteristics of image, several defog techniques were investigated. The Spatial Random Markov Fields with Boundary Constraints was proposed, which performs on image within the space and patch using boundary constraints. Experimental results demonstrate that the proposed work is efficient to remove fog, restore space of an image, and preserve the natural atmospheric light even in foggy days without changing the color.