Bridging Vision and Language: From Image Restoration to Robust Document Intelligence

Abstract

The deployment of artificial intelligence in real-world environments is often hindered by environmental noise and signal degradation, which significantly compromise the accuracy of visual perception tasks. Establishing robust AI capable of operating under these non-ideal conditions is critical for ensuring reliability and scalability in practical applications. Since the ability to restore natural images from degraded inputs serves as a fundamental prerequisite for subsequent perception and reasoning, addressing fidelity at the signal level is essential for downstream performance.

A hierarchical methodology is employed to improve fidelity across a complete processing arc, moving systematically from raw signal restoration to high-level reasoning. Initial efforts focus on pixel-level restoration through advanced dehazing techniques to recover clean visual data from noisy environments. This provides the basis for extracting structural components and the eventual synthesis of semantic understanding by bridging vision and language modalities. Results demonstrate that this integrated pipeline successfully enhances fidelity from raw signals to complex reasoning, providing a robust framework for AI systems to navigate and interpret noisy real-world scenarios with high accuracy.

Biography

Dr. Zahra Anvari is an AI Researcher with expertise in deep learning, large language models (LLMs), and multimodal document understanding. She earned her PhD in Computer Science from the University of Texas at Arlington, where her research centered on deep learning and computer vision. With over three years of industry experience, she has led AI research for large-scale document intelligence and built vision-language pipelines for the analysis of complex documents. Dr. Anvari has authored several papers in peer-reviewed journals and conferences and serves in the academic community as a reviewer and committee member for venues such as WACV, IEEE TIP, Neurocomputing, and ISVC.