梯度作为条件：重新思考HOG用于全场景图像恢复
Gradient as Conditions: Rethinking HOG for All-in-one Image Restoration
AI总结：
HOGformer通过整合可学习的HOG特征，利用动态HOG感知自注意力机制和DIFF模块，实现退化感知的图像恢复，取得最佳性能并具有良好的泛化能力。
AI中文摘要：
全场景图像恢复（AIR）旨在通过利用信息丰富的退化条件来指导恢复过程，在统一模型中解决多样的退化问题。然而，现有方法往往依赖于隐式学习的先验知识，这可能会使特征表示交织并阻碍在复杂或未见过的场景中的性能。作为经典的梯度表示，直方图梯度（HOG）我们观察到它在多种退化中具有强大的判别能力，使其成为AIR的强大且可解释的先验。基于这一见解，我们提出了HOGformer，一种基于Transformer的模型，该模型整合了可学习的HOG特征以实现退化感知的恢复。HOGformer的核心是一种动态HOG感知自注意力（DHOGSA）机制，该机制根据由HOG描述符编码的退化特定提示，自适应地建模长距离空间依赖性。为了进一步适应AIR中退化异质性，我们提出了动态交互前馈（DIFF）模块，该模块促进了通道-空间交互，使在多种退化下实现稳健的特征转换。此外，我们提出了HOG损失以显式增强结构保真度和边缘锐度。在各种基准上的广泛实验，包括恶劣天气和自然退化，证明了HOGformer实现了最先进的性能，并且在复杂的真实世界场景中具有良好的泛化能力。代码可在https://github.com/Fire-friend/HOGformer上获得。
英文摘要：
All-in-one image restoration (AIR) aims to address diverse degradations within a unified model by leveraging informative degradation conditions to guide the restoration process. However, existing methods often rely on implicitly learned priors, which may entangle feature representations and hinder performance in complex or unseen scenarios. Histogram of Oriented Gradients (HOG) as a classical gradient representation, we observe that it has strong discriminative capability across diverse degradations, making it a powerful and interpretable prior for AIR. Based on this insight, we propose HOGformer, a Transformer-based model that integrates learnable HOG features for degradation-aware restoration. The core of HOGformer is a Dynamic HOG-aware Self-Attention (DHOGSA) mechanism, which adaptively models long-range spatial dependencies conditioned on degradation-specific cues encoded by HOG descriptors. To further adapt the heterogeneity of degradations in AIR, we propose a Dynamic Interaction Feed-Forward (DIFF) module that facilitates channel-spatial interactions, enabling robust feature transformation under diverse degradations. Besides, we propose a HOG loss to explicitly enhance structural fidelity and edge sharpness. Extensive experiments on a variety of benchmarks, including adverse weather and natural degradations, demonstrate that HOGformer achieves state-of-the-art performance and generalizes well to complex real-world scenarios.Code is available at https://github.com/Fire-friend/HOGformer.
