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

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.

Adapt CLIP as Aggregation Instructor for Image Dehazing

Most dehazing methods suffer from limited receptive field and do not explore the rich semantic prior encapsulated in vision-language models, which have proven effective in downstream tasks. In this paper, we introduce CLIPHaze, a pioneering hybrid framework that synergizes the efficient global modeling of Mamba with the prior knowledge and zero-shot capabilities of CLIP to address both issues simultaneously. Specifically, our method employs parallel state space model and window-based self-attention to obtain global contextual dependency and local fine-grained perception, respectively. To seamlessly aggregate information from both paths, we introduce CLIP-instructed Aggregation Module (CAM). For non-homogeneous and homogeneous haze, CAM leverages zero-shot estimated haze density map and high-quality image embedding without degradation information to explicitly and implicitly determine the optimal neural operation range for each pixel, thereby adaptively fusing two paths with different receptive fields. Extensive experiments on various benchmarks demonstrate that CLIPHaze achieves state-of-the-art (SOTA) performance, particularly in non-homogeneous haze. Code will be publicly after acceptance.