Description

Scientific imaging is essential in fields like biology, physics, and materials science, where high-resolution images reveal critical structures and phenomena. However, obtaining such images is often limited by hardware, exposure risks, and time constraints. Super-resolution (SR) techniques aim to overcome these limitations by reconstructing high-resolution images from low-resolution inputs.

While traditional and deep learning-based SR methods have shown promise, they often fail in scientific contexts due to poor structural preservation, lack of contextual awareness, and the presence of artifacts. Scientific imaging requires reconstructions that are not only visually realistic but also structurally accurate and domain-aware.

Motivation

Current SR methods struggle with:

• Preserving fine structural details,
• Capturing long-range dependencies,
• Integrating semantic context,
• And avoiding artifacts in domain-specific data.

These issues limit their effectiveness in scientific applications where precision is critical.

Research Objectives

This work aims to develop a super-resolution framework tailored for scientific imaging with the following objectives:

• Design a structure-informed, multi-scale GAN architecture,
• Integrate semantic feature conditioning for contextual awareness,
• Propose a comprehensive loss function for perceptual and structural fidelity,
• Validate the approach on scientific imaging datasets.