论文标题
图像修复的金字塔注意网络
Pyramid Attention Networks for Image Restoration
论文作者
论文摘要
自相似性是指在图像恢复算法中广泛使用的图像，这些算法很小但类似的模式往往发生在不同的位置和尺度上。但是，最近的高级深度卷积神经网络基于图像恢复的方法并不能通过依靠仅在相同规模上处理信息的自我发挥神经模块来充分利用自相似性。为了解决这个问题，我们提出了一个新型的金字塔注意模块，用于图像恢复，该模块捕获了多尺度特征金字塔的远程特征对应关系。受到损坏（例如噪声或压缩工件）在更粗糙的图像尺度上大幅下降的事实的启发，我们的注意力模块旨在能够从其在较矮的层面上从其“干净”通信中借用清洁信号。提出的金字塔注意模块是一个通用的构件，可以灵活地集成到各种神经体系结构中。通过对多个图像恢复任务进行的广泛实验来验证其有效性：图像降低，表演，压缩伪影减少和超级分辨率。如果没有任何铃铛和哨子，我们的锅盘（带有简单网络骨架的金字塔注意模块）可以以优越的准确性和视觉质量产生最新的结果。我们的代码将在https://github.com/shi-labs/pyramid-criptition-networks上找到
Self-similarity refers to the image prior widely used in image restoration algorithms that small but similar patterns tend to occur at different locations and scales. However, recent advanced deep convolutional neural network based methods for image restoration do not take full advantage of self-similarities by relying on self-attention neural modules that only process information at the same scale. To solve this problem, we present a novel Pyramid Attention module for image restoration, which captures long-range feature correspondences from a multi-scale feature pyramid. Inspired by the fact that corruptions, such as noise or compression artifacts, drop drastically at coarser image scales, our attention module is designed to be able to borrow clean signals from their "clean" correspondences at the coarser levels. The proposed pyramid attention module is a generic building block that can be flexibly integrated into various neural architectures. Its effectiveness is validated through extensive experiments on multiple image restoration tasks: image denoising, demosaicing, compression artifact reduction, and super resolution. Without any bells and whistles, our PANet (pyramid attention module with simple network backbones) can produce state-of-the-art results with superior accuracy and visual quality. Our code will be available at https://github.com/SHI-Labs/Pyramid-Attention-Networks
