Colorization of Black and White Images Using a Hybrid Deep Learning Framework

Abstract

With the development of deep learning algorithms and their great success in the field of computer vision, the field of automatic image colorization has witnessed significant improvements in accuracy and realism. This study introduces a novel deep learning-based method for colorizing black and white photographs, utilizing the powerful feature extraction of the InceptionResNetV2 model and the generative capabilities of autoencoders. A custom data generator was developed for efficient preprocessing, data augmentation, and batch processing, enhancing memory usage and scalability. The system encodes grayscale images and extracts high-level features, which are then fused and decoded into two color channels, combined with the original luminance to recreate the image in the LAB color space. The method demonstrates strong performance with a PSNR of 22.8154 and a SSIM of 0.9097, showcasing its potential for applications like historical image restoration and media enhancement.