A Deep Generative Framework for Realistic Colorization of Grayscale Astronomical Images

Abstract

Astronomical images captured using Space based or Ground based telescopes are majorly in grayscale, hiding the minute details of the intricate structures. Our paper presents a novel idea of the implementation of a Generative Adversarial Network (GAN) - based framework for astronomical image colorization. Our proposed model utilizes a U-Net-based generator and a PatchGAN discriminator to learn realistic color schemes from the grayscale input. The system was trained on the Hubble Space Telescope data set collected from NASA and ESA archives, comprising 673 high-resolution images, with an additional 99 images reserved for testing. The images are restricted to a resolution of 512 x 512 pixels due to computational limitations. Quantitative evaluation demonstrates that our proposed framework achieves an average Peak Signal-to-Noise Ratio (PSNR) of 31.46 dB, Structural Similarity Index Measure (SSIM) of 0.92, and Signal-to-Noise Ratio (SNR) of 26.85 dB which outperforms traditional approaches. The experimental results confirm that the model produces perceptually accurate, visually coherent, and scientifically meaningful and colorized outputs which contribute to enhanced visualization and analysis of astronomical data.