论文标题
轻巧图像超级分辨率的混合像素无修理网络
Hybrid Pixel-Unshuffled Network for Lightweight Image Super-Resolution
论文作者
论文摘要
卷积神经网络（CNN）在图像超分辨率（SR）上取得了巨大成功。但是，大多数基于CNN的SR模型都采用大量计算以获得高性能。多分辨率融合的降采样功能是提高视觉识别性能的有效方法。尽管如此，它在SR任务中仍然是违反直觉的，该任务需要将低分辨率输入投影到高分辨率。在本文中，我们提出了一个新型混合像素无修理网络（HPUN），通过将一个有效有效的下采样模块引入SR任务中。该网络包含像素无修整的下采样和自由分离的可分离卷积。具体而言，我们利用像素 - 固定操作来简化输入功能，并使用分组的卷积来减少通道。此外，我们通过将输入功能添加到其输出中来增强深度卷积的性能。基准数据集的实验表明，我们的HPUN实现并超过了最先进的重建性能，而参数和计算成本较少。
Convolutional neural network (CNN) has achieved great success on image super-resolution (SR). However, most deep CNN-based SR models take massive computations to obtain high performance. Downsampling features for multi-resolution fusion is an efficient and effective way to improve the performance of visual recognition. Still, it is counter-intuitive in the SR task, which needs to project a low-resolution input to high-resolution. In this paper, we propose a novel Hybrid Pixel-Unshuffled Network (HPUN) by introducing an efficient and effective downsampling module into the SR task. The network contains pixel-unshuffled downsampling and Self-Residual Depthwise Separable Convolutions. Specifically, we utilize pixel-unshuffle operation to downsample the input features and use grouped convolution to reduce the channels. Besides, we enhance the depthwise convolution's performance by adding the input feature to its output. Experiments on benchmark datasets show that our HPUN achieves and surpasses the state-of-the-art reconstruction performance with fewer parameters and computation costs.
