Conditional Stable Diffusion for Distortion Correction and Image Rectification

Abstract

Image rectification and distortion correction are fundamental tasks in the field of image processing and computer vision, with it is applications ranging from document processing to medical imaging. This study presents a novel Conditional Stable Diffusion framework designed to tackle the challenges posed by diverse types of image distortions. Unlike existing traditional methods, our approach introduces an adaptive diffusion process that customizes its behavior based on the specific characteristics of the input image. By introducing controlled noise in a bidirectional manner, our model learns to interpret and refine various distortion patterns and progressively refines the image into a more uniform distribution. Furthermore, to complement the diffusion process, we incorporate a Guided Rectification Network (GRN) that generates reliable conditions from the input image, effectively reducing ambiguity between the distorted and target outputs. The integration of stable diffusion is justified by its versatility in handling diverse types and degrees of distortion. Our proposed method effectively handles a wide range of distortions—including projective and complex lens-based distortions such as barrel and pincushion—by dynamically adapting to each unique distortion type. Whether stemming from lens abnormalities, perspective discrepancies, or other factors, our proposed stable diffusion-based method consistently adapts to the specific characteristics of the distortion, yielding superior outcomes. Experimental results across benchmark datasets demonstrate that our method consistently outperforms existing state-of-the-art approaches. Additionally, we highlight that our work is the first instance of the diffusion method being used to simultaneously address various distortion types (barrel, pincushion, lens, etc.) for multi-distortion image rectification. This Conditional Stable Diffusion framework thus offers a promising advancement for robust and versatile image distortion correction.