Imagine being able to zoom in on an image by 100x without losing any quality. That’s the goal of extreme single-image super-resolution (SISR), and researchers are making rapid progress. I’m fascinated by the potential of SISR to transform industries like materials science, where understanding textures and materials properties is crucial.
Currently, I’m investigating state-of-the-art techniques for SISR, focusing on domain-specific texture synthesis for materials. I’m training models on curated datasets and exploring the feasibility of fine-tuning generative models like ESRGAN. One promising approach is conditional generation, where semantic guidance (e.g., material property tags like ‘shiny’ or ‘rough’) can be used to steer the output.
If you’re working in this field or have experience with SISR, I’d love to hear about your approaches and recommendations. What are some relevant literature, model architectures, or alternative methods that I should be considering?
The possibilities of SISR are vast, and I’m excited to see where this technology takes us.