This paper introduces an innovative blind in-painting technique designed for image quality enhancement and noise removal. Employing Monte-Carlo simulations, the proposed method approximates the optimal mask necessary for automatic image in-painting. This involves the progressive construction of a noise removal mask, initially sampled randomly from a binomial distribution. A confidence map is iteratively generated, providing a pixel-wise indicator map that discerns whether a particular pixel resides within the dataset domain. Notably, the proposed method eliminates the manual creation of an image mask to eradicate noise, a process prone to additional time overhead, especially when noise is dispersed across the entire image. Furthermore, the proposed method simplifies the determination of pixels involved in the in-painting process, excluding normal pixels and thereby preserving the integrity of the original image content. Computer simulations demonstrate the efficacy of this method in removing various types of noise, including brush painting and random salt and pepper noise. The proposed technique successfully restores similarity between the original and normalized datasets, yielding a Binary Cross Entropy (BCE) of 0.69 and a Peak-Signal-to-Noise-Ratio (PSNR) of 20.069. With its versatile applications, this method proves beneficial in diverse industry and medical contexts.