SEM-Net：基于空间增强状态空间模型的高效像素级图像修复网络
SEM-Net: Efficient Pixel Modelling for image inpainting with Spatially Enhanced SSM
AI总结：
提出SEM-Net，一种基于状态空间模型的像素级图像修复网络，通过蛇形曼巴块和空间增强前馈网络捕获长距离依赖，在修复和运动去模糊任务上达到最先进性能。
摘要：
图像修复旨在根据图像已知区域的信息修复部分受损的图像。实现语义上合理的修复结果尤其具有挑战性，因为这要求重建区域表现出与语义一致区域相似的模式。这要求模型具备强大的捕获长距离依赖关系的能力。现有模型在这方面存在困难，原因在于基于卷积神经网络（CNN）的方法感受野增长缓慢，而基于Transformer的方法采用补丁级交互，这些方法在捕获长距离依赖方面效率低下。受此启发，我们提出了SEM-Net，一种新颖的视觉状态空间模型（SSM）网络，在像素级别对损坏图像进行建模，同时在状态空间中捕获长距离依赖关系（LRDs），实现了线性计算复杂度。为了解决SSM固有的空间感知不足问题，我们引入了蛇形曼巴块（SMB）和空间增强前馈网络。这些创新使SEM-Net在两个不同数据集上优于最先进的修复方法，在捕获LRDs和增强空间一致性方面显示出显著改进。此外，SEM-Net在运动去模糊方面也达到了最先进的性能，证明了其泛化能力。我们的源代码将在https://github.com/ChrisChen1023/SEM-Net发布。
英文摘要：
Image inpainting aims to repair a partially damaged image based on the information from known regions of the images. \revise{Achieving semantically plausible inpainting results is particularly challenging because it requires the reconstructed regions to exhibit similar patterns to the semanticly consistent regions}. This requires a model with a strong capacity to capture long-range dependencies. Existing models struggle in this regard due to the slow growth of receptive field for Convolutional Neural Networks (CNNs) based methods and patch-level interactions in Transformer-based methods, which are ineffective for capturing long-range dependencies. Motivated by this, we propose SEM-Net, a novel visual State Space model (SSM) vision network, modelling corrupted images at the pixel level while capturing long-range dependencies (LRDs) in state space, achieving a linear computational complexity. To address the inherent lack of spatial awareness in SSM, we introduce the Snake Mamba Block (SMB) and Spatially-Enhanced Feedforward Network. These innovations enable SEM-Net to outperform state-of-the-art inpainting methods on two distinct datasets, showing significant improvements in capturing LRDs and enhancement in spatial consistency. Additionally, SEM-Net achieves state-of-the-art performance on motion deblurring, demonstrating its generalizability. Our source code will be released in https://github.com/ChrisChen1023/SEM-Net.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
