Deep Learning Based Panchromatic2RGB Image Generation from VHR Images

Image colorization is the process of obtaining colored images by assigning RGB color values to a grayscale or panchromatic image. This technique has an important place in the field of computer vision because colored images provide a better visual experience and are widely used in areas such as image recognition and object detection. It also has many practical applications such as coloring historical photographs, adding colors to be used in the analysis of medical images, and improving the analysis of satellite images. Colorization methods are divided into two main categories: brush coloring and sample-based coloring. Both methods have certain limitations. The performance of these methods depends on the selected reference images and may sometimes contain false colors or significant errors. While these methods require operator intervention or pre-defined rules, deep learning based methods are largely automated and uses neural networks to understand the global and local context of an image, leading to more realistic and contextually accurate colorizations. The presented study uses the Denoising Diffusion Null-Space Model (DDNM) architecture. DDNM is a method that aims to obtain more efficient and high-quality results compared to the coloring approaches available in literature. In the study, the weight data of the DDNM architecture was used to predict colored images from panchromatic images using the SpaceNet 6 open access dataset. The SpaceNet 6 dataset includes a combination of Capella Space 0.5m Synthetic Aperture Radar (SAR) imagery and Maxar's 0.5m electro-optical (EO) imagery. In order to assess the results, Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) accuracy metrics are calculated.