SDAN：用于学习失调光学变焦的平方可变形对准网络
SDAN: Squared Deformable Alignment Network for Learning Misaligned Optical Zoom
摘要：
基于深度神经网络（DNN）的超分辨率算法极大地提高了生成图像的质量。然而，这些算法往往会产生显着的文物时，处理现实世界的超分辨率问题，由于学习失调的光学变焦的困难。在本文中，我们引入了一个平方变形对齐网络（SDAN）来解决这个问题。我们的网络学习卷积核的每点偏移量的平方，然后根据偏移量在校正的卷积窗口中对齐特征。因此，未对准将通过所提取的对准特征来最小化。与普通可变形卷积网络（DCN）中使用的逐点偏移不同，我们提出的平方偏移不仅加速了偏移学习，而且还用更少的参数提高了生成质量。此外，我们进一步提出了一个有效的交叉包装注意层，以提高学习的偏移的准确性。它利用打包和解包操作来扩大偏移学习的感受野，并增强提取低分辨率图像与参考图像之间的空间连接的能力。综合实验表明，我们的方法优于其他国家的最先进的方法在计算效率和现实的细节。
英文摘要：
Deep Neural Network (DNN) based super-resolution algorithms have greatly improved the quality of the generated images. However, these algorithms often yield significant artifacts when dealing with real-world super-resolution problems due to the difficulty in learning misaligned optical zoom. In this paper, we introduce a Squared Deformable Alignment Network (SDAN) to address this issue. Our network learns squared per-point offsets for convolutional kernels, and then aligns features in corrected convolutional windows based on the offsets. So the misalignment will be minimized by the extracted aligned features. Different from the per-point offsets used in the vanilla Deformable Convolutional Network (DCN), our proposed squared offsets not only accelerate the offset learning but also improve the generation quality with fewer parameters. Besides, we further propose an efficient cross packing attention layer to boost the accuracy of the learned offsets. It leverages the packing and unpacking operations to enlarge the receptive field of the offset learning and to enhance the ability of extracting the spatial connection between the low-resolution images and the referenced images. Comprehensive experiments show the superiority of our method over other state-of-the-art methods in both computational efficiency and realistic details.
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。
