Speaker
Description
Colorizing monochromatic images is crucial for enhancing the informativeness of visual data in modern information technology and computer vision systems. However, automatic colorization poses an inherently ill-posed mathematical challenge due to the multimodal nature of color distributions, where multiple valid color mappings exist for any given grayscale input. This article conducts a comparative analysis of classical algorithms (such as the Welch and Levin methods) against advanced deep learning pipelines. The evaluated architectures range from baseline convolutional neural networks (CNNs) and standalone U-Net models to generative adversarial networks (GANs) and a novel hybrid Fusion GAN that integrates global semantic priors extracted via a ResNet-18 backbone.
All models were trained and rigorously evaluated in the CIE Lab* color space, which effectively separates luminance from chrominance. To ensure a robust evaluation, experiments were conducted using a diverse benchmark of 2,000 images from the COCO dataset. Quality assessment combined traditional metrics like Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index
(SSIM) with the Learned Perceptual Image Patch Similarity (LPIPS) metric, prioritizing human-like visual fidelity.
Experimental results yielded counterintuitive insights. While the proposed Fusion GAN successfully surpassed classical methods, baseline CNNs, and standard GANs across most benchmarks, the standalone U-Net architecture secured the highest overall ranking. Specifically, U-Net achieved the top SSIM score of 0.945 (indicating superior structural preservation), the lowest LPIPS of 0.180 (best perceptual quality), and the fastest inference speed, enabling real-time applications. These findings challenge prevailing assumptions about model complexity, demonstrating that simpler encoder-decoder designs can outperform more intricate generative models in quantitative image restoration tasks. Ultimately, this study underscores the critical need for multifaceted evaluation frameworks in deep learning research.
Keywords: computer vision, image colorization, deep learning, GANs, U-Net, CNNs, LPIPS, SSIM, PSNR, COCO dataset