Local and non-local attention-based methods have been well studied in various image restoration tasks while leading to promising performance. However, most of the existing methods solely focus on one type of attention mechanism (local or non-local). Furthermore, by exploiting the self-similarity of natural images, existing pixel-wise non-local attention operations tend to give rise to deviations in the process of characterizing long-range dependence due to image degeneration. To overcome these problems, in this paper we propose a novel collaborative attention network (COLA-Net) for image restoration, as the first attempt to combine local and non-local attention mechanisms to restore image content in the areas with complex textures and with highly repetitive details respectively. In addition, an effective and robust patch-wise non-local attention model is developed to capture long-range feature correspondences through 3D patches. Extensive experiments on synthetic image denoising, real image denoising and compression artifact reduction tasks demonstrate that our proposed COLA-Net is able to achieve state-of-the-art performance in both peak signal-to-noise ratio and visual perception, while maintaining an attractive computational complexity. The source code is available on https://github.com/MC-E/COLA-Net.
翻译:在各种图像恢复任务中,地方和非地方关注方法已经很好地研究过各种图像恢复任务,并导致有希望的业绩;然而,大多数现有方法仅侧重于一种类型的关注机制(当地或非当地);此外,通过利用自然图像的自相类似性,现有的像素异的非地方关注行动往往会导致由于图像变形而导致长期依赖性特征的出现偏差;为了克服这些问题,我们在本文件中提议建立一个用于图像恢复的新颖的合作关注网络(COLA-Net),作为将地方和非地方关注机制结合起来的首次尝试,以便分别以复杂的纹理和高度重复的细节恢复这些地区的图像内容;此外,通过3D补版,开发一个有效和稳健的补足性非地方关注模式,以捕捉长距离的特征通信;对合成图像分层、真实图像解析和压缩工艺品减少任务进行广泛的实验,表明我们拟议的COLA-Net能够实现最高级信号至噪音比率和视觉认知的状态性业绩,同时保持具有吸引力的计算复杂性。