Rectification Reimagined: A Unified Mamba Model for Image Correction and Rectangling with Prompts

• URL: http://arxiv.org/abs/2512.18718v1
• Date: Sun, 21 Dec 2025 12:33:44 GMT
• Title: Rectification Reimagined: A Unified Mamba Model for Image Correction and Rectangling with Prompts
• Authors: Linwei Qiu, Gongzhe Li, Xiaozhe Zhang, Qinlin Sun, Fengying Xie,
• Abstract summary: We introduce the Unified Rectification Framework (UniRect), a comprehensive approach that addresses these practical tasks from a consistent distortion rectification perspective.<n>Our approach incorporates various task-specific inverse problems into a general distortion model by simulating different types of lenses.<n>Our models have achieved state-of-the-art performance compared with other up-to-date methods.
• Score: 7.136884388888679
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image correction and rectangling are valuable tasks in practical photography systems such as smartphones. Recent remarkable advancements in deep learning have undeniably brought about substantial performance improvements in these fields. Nevertheless, existing methods mainly rely on task-specific architectures. This significantly restricts their generalization ability and effective application across a wide range of different tasks. In this paper, we introduce the Unified Rectification Framework (UniRect), a comprehensive approach that addresses these practical tasks from a consistent distortion rectification perspective. Our approach incorporates various task-specific inverse problems into a general distortion model by simulating different types of lenses. To handle diverse distortions, UniRect adopts one task-agnostic rectification framework with a dual-component structure: a {Deformation Module}, which utilizes a novel Residual Progressive Thin-Plate Spline (RP-TPS) model to address complex geometric deformations, and a subsequent Restoration Module, which employs Residual Mamba Blocks (RMBs) to counteract the degradation caused by the deformation process and enhance the fidelity of the output image. Moreover, a Sparse Mixture-of-Experts (SMoEs) structure is designed to circumvent heavy task competition in multi-task learning due to varying distortions. Extensive experiments demonstrate that our models have achieved state-of-the-art performance compared with other up-to-date methods.

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