Automated Colorization of Grayscale Images Using Texture Descriptors

Abstract A novel example-based process for automated colorization of grayscale images using texture descriptors (ACTD) without any human intervention is proposed. By analyzing a set of sample color images, coherent regions of homogeneous textures are extracted. A multi-channel filtering technique is used for texture-based image segmentation. For each area of interest, state of the art texture descriptors are then computed and stored, along with corresponding color information. These texture descriptors and the color information are used for colorization of a grayscale image with similar textures. Given a grayscale image to be colorized, the segmentation and feature extraction processes are repeated. The texture descriptors are used to perform Content-Based Image Retrieval (CBIR). The colorization process is performed by chroma replacement. This research finds numerous applications, ranging from classic film restoration and enhancement, to adding valuable information into medical and satellite imaging, and to enhance the detection of objects from x-ray images at the airports. Computer vision; Image colorization; Fuzzy C-means clustering; Gabor transform; CBI