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, combined with a modified Fuzzy C-means (FCM) clustering algorithm. This modified FCM clustering algorithm includes both the local spatial information from neighboring pixels, and the spatial Euclidian distance to the cluster’s center of gravity. 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. Also, this can be used to enhance the detection of objects from x-ray images at the airports.
Highlights
Image colorization has been performed through various means since the early 20th century, as a very laborious, time-consuming, subjective and painstaking manual process
In order to improve the tolerance to noise of the Fuzzy C-means clustering algorithm, Krinidis and Chatzis [12] have proposed a new method by introducing the novel Gki factor
A new and innovative ACTD method for automating example-based colorization process is implemented. This process combines several state-of-the-art techniques from Digital Image Processing in order to improve the automation of the colorization process
Summary
Image colorization has been performed through various means since the early 20th century, as a very laborious, time-consuming, subjective and painstaking manual process. Current methods of image colorization can be classified into two different groups depending on the approach being used Example-based colorization techniques automate this process by providing an example image from which to extract the color information [2,3,4]. This method can save a lot of time and requires little or no user interaction. The method suggested by Irony et al [4] used a very robust monochrome texture matching method with spatial filtering They suggested that better results could be obtained by using improved spatial coherence descriptors, such as the Gabor transform. Several other research papers suggested that better segmentation could be achieved by using Gabor filters
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