论文标题
ARM：任何时候
ARM: Any-Time Super-Resolution Method
论文作者
论文摘要
本文提出了一种任何时间的超分辨率方法（ARM），以解决过度参数化的单图像超分辨率（SISR）模型。我们的手臂是由三个观察结果激励的：（1）不同图像贴片的性能随不同大小的SISR网络而变化。 （2）计算开销与重建图像的性能之间存在权衡。 （3）给定输入图像，其边缘信息可以是估计其PSNR的有效选择。随后，我们训练包含不同尺寸的SISR子网的手臂超网，以处理各种复杂性的图像贴片。为此，我们构建了一个边缘到PSNR查找表，该表将图像补丁的边缘得分映射到每个子网的PSNR性能，以及子网的一组计算成本。在推论中，图像贴片单独分配给不同的子网，以进行更好的计算绩效折衷。此外，每个SISR子网都共享手臂超网的权重，因此不引入额外的参数。多个子网的设置可以很好地使SISR模型的计算成本适应动态可用的硬件资源，从而使SISR任务随时进行。在不同大小的分辨率数据集的广泛实验和流行的SISR网络作为骨架作为骨架上验证了我们的手臂的有效性和多功能性。源代码可在https://github.com/chenbong/arm-net上找到。
This paper proposes an Any-time super-Resolution Method (ARM) to tackle the over-parameterized single image super-resolution (SISR) models. Our ARM is motivated by three observations: (1) The performance of different image patches varies with SISR networks of different sizes. (2) There is a tradeoff between computation overhead and performance of the reconstructed image. (3) Given an input image, its edge information can be an effective option to estimate its PSNR. Subsequently, we train an ARM supernet containing SISR subnets of different sizes to deal with image patches of various complexity. To that effect, we construct an Edge-to-PSNR lookup table that maps the edge score of an image patch to the PSNR performance for each subnet, together with a set of computation costs for the subnets. In the inference, the image patches are individually distributed to different subnets for a better computation-performance tradeoff. Moreover, each SISR subnet shares weights of the ARM supernet, thus no extra parameters are introduced. The setting of multiple subnets can well adapt the computational cost of SISR model to the dynamically available hardware resources, allowing the SISR task to be in service at any time. Extensive experiments on resolution datasets of different sizes with popular SISR networks as backbones verify the effectiveness and the versatility of our ARM. The source code is available at https://github.com/chenbong/ARM-Net.
