Conditional Diffusion Transformer for Unified Distortion Correction and Rectification

Image distortion correction and rectification are essential tasks in computer vision, addressing challenges such as lens aberrations, geometric distortions, and perspective errors that compromise image quality and usability. Despite advancements, existing methods often struggle with handling complex cases involving multiple distortions simultaneously. These tasks hold significant importance in applications like document processing, medical imaging, and autonomous systems. We propose a novel Conditioned-Guided Diffusion Transformer designed to correct diverse and complex distortions. Our approach combines the strengths of conditional diffusion transformer architectures, leveraging the visual inputs to guide the rectification process. The approach integrates conditional diffusion transformers with a coarse-to-fine rectification strategy. Initially, the input image undergoes pre-processing using Thin Plate Spline transformations, estimating control points and generating a mesh grid for coarse rectification. The semi-rectified image is then refined through a novel Flow Prediction Module, which computes the flow field required for further warping. Finally, a pretrained Latent Conditional Diffusion Transformer produces the fully rectified output. Comprehensive evaluations on benchmark datasets reveal that proposed work consistently surpasses state-of-the-art methods across various distortion types, including fisheye, barrel, skew, and perspective distortions. Its adaptability and superior results highlight it’s potential as a robust solution for real-world distortion correction and rectification tasks.