This project focuses on automatic image colorization, a challenging image processing task that converts grayscale images into realistic colored images without requiring user intervention. Two fully automated approaches were implemented and evaluated. The first approach utilizes Support Vector Regression (SVR) combined with Markov Random Fields (MRF) to predict color channels for grayscale pixels. The second approach employs a Convolutional Neural Network (CNN) based deep learning model to predict color values in the LAB color space before converting them into RGB images. Experimental analysis using approximately 200 trained datasets showed that the CNN-based approach generated more realistic and accurate colorizations, especially for images containing multiple objects. The outputs were evaluated using histogram comparison techniques such as Correlation, Chi-square, Intersection, and Bhattacharyya distance. This project demonstrates the effectiveness of deep learning techniques in advanced image restoration and enhancement tasks.