Images tend to degenerate over time and are exposed to noises. These noises don’t only affect the visual outlook but also hampers the allied significance to these images. Different techniques are being put to use for de-noising such images. Image in-painting is one such phenomenon of denoising the images that involves approximating the de-noised form of the image. The current study aims at developing an in-painting system for restoration of lost art, reconstruction of destroyed images and removal of unnecessary objects. The motivation for the same has been driven from Partial differential equation based anisotropic diffusion model or image in-painting. The steady state heat equation or the Laplace equation has been used to model and approximate the de-noised data for noised region of the image. The Laplace equation has been used clubbed with the Dirichlet boundary conditions in order to fill in the degenerated or the noised region. The Dirichlet conditions provides a firm starting point for approximating the structural framework of the noised region in order to remove the inconsistencies using the colour intensities of the neighboring pixels as the boundary conditions. The colour intensities are modeled to be diffusive in nature. The experiments conducted using the proposed approach portray significant speedups and presents a practicable in-painting strategy.