Recently attention-based networks have been successful for image restoration tasks. However, existing methods are either computationally expensive or have limited receptive fields, adding constraints to the model. They are also less resilient in spatial and contextual aspects and lack pixel-to-pixel correspondence, which may degrade feature representations. In this paper, we propose a novel and computationally efficient architecture Single Stage Adaptive Multi-Attention Network (SSAMAN) for image restoration tasks, particularly for image denoising and image deblurring. SSAMAN efficiently addresses computational challenges and expands receptive fields, enhancing robustness in spatial and contextual feature representation. Its Adaptive Multi-Attention Module (AMAM), which consists of Adaptive Pixel Attention Branch (APAB) and an Adaptive Channel Attention Branch (ACAB), uniquely integrates channel and pixel-wise dimensions, significantly improving sensitivity to edges, shapes, and textures. We perform extensive experiments and ablation studies to validate the performance of SSAMAN. Our model shows state-of-the-art results on various benchmarks, for example, on image denoising tasks, SSAMAN achieves a notable 40.08 dB PSNR on SIDD dataset, outperforming Restormer by 0.06 dB PSNR, with 41.02% less computational cost, and achieves a 40.05 dB PSNR on the DND dataset. For image deblurring, SSAMAN achieves 33.53 dB PSNR on GoPro dataset. Code and models are available at Github.

中文翻译：

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用于图像恢复的单阶段自适应多注意网络

最近，基于注意力的网络在图像恢复任务中取得了成功。然而，现有方法要么计算成本昂贵，要么感受野有限，从而给模型增加了约束。它们在空间和上下文方面的弹性也较差，并且缺乏像素到像素的对应，这可能会降低特征表示。在本文中，我们提出了一种新颖且计算高效的架构单级自适应多注意网络（SSAMAN），用于图像恢复任务，特别是图像去噪和图像去模糊。 SSAMAN 有效地解决了计算挑战并扩展了感受野，增强了空间和上下文特征表示的鲁棒性。其自适应多注意模块（AMAM）由自适应像素注意分支（APAB）和自适应通道注意分支（ACAB）组成，独特地集成了通道和像素维度，显着提高了对边缘、形状和纹理的敏感度。我们进行了大量的实验和消融研究来验证 SSAMAN 的性能。我们的模型在各种基准上显示了最先进的结果，例如，在图像去噪任务上，SSAMAN 在 SIDD 数据集上实现了显着的 40.08 dB PSNR，比 Restormer 好 0.06 dB PSNR，计算成本降低了 41.02%，并实现了DND 数据集上的 PSNR 为 40.05 dB。对于图像去模糊，SSAMAN 在 GoPro 数据集上实现了 33.53 dB PSNR。代码和模型可在 Github 上获取。