Research On GPU-accelerated Near Real-time Grayscale Image Colorization

Image colorization has important research significance in the field of image processing. Colorization process can be regarded as the process of adding color information to a grayscale image. The image can contain more information with the addition of color information, and thus the colorization technology is now widely used in many fields of medicine,industry, film and other fields.User interactions are required in order to complete the colorization optimization process. Existing methods only focuses on the effect of colorization but ignores the processing performance, which seriously affecting the users' interactive experiences. In order to take into account both of the effect of image colorization and processing performance, this article proposes a novel GPU-accelerated near real-time image colorization method. By supporting a GPU-accelerated generation of colorization masks, the colorization results can be displayed in near real-time. In order to avoid duplicate calculation, the method is divided into the pre-processing step and the interactive processing step. By using the patch-based PatchMatch algorithm, nearest neighboring pixels of each pixel on the global image space are found efficiently. To ensure the bidirectional equal propagation of the color between neighboring pixels,we propose a parallel construction algorithm for the compressed symmetry sparse matrix, which can improve the processing performance of the colorization and reduce the consumption of storage space. In the interactive processing step, the GPU-based conjugate gradient method is used to solve the sparse linear equations to produce colorized results.The proposed image colorization method is highly parallel and can be implemented using CUDA. The experimental results show that the proposed method can not only produce high-quality colorized images but also achieve near real-time performance.