Lightweight and efficiency are critical drivers for the practical application of image super-resolution (SR) algorithms. We propose a simple and effective approach, ShuffleMixer, for lightweight image super-resolution that explores large convolution and channel split-shuffle operation. In contrast to previous SR models that simply stack multiple small kernel convolutions or complex operators to learn representations, we explore a large kernel ConvNet for mobile-friendly SR design. Specifically, we develop a large depth-wise convolution and two projection layers based on channel splitting and shuffling as the basic component to mix features efficiently. Since the contexts of natural images are strongly locally correlated, using large depth-wise convolutions only is insufficient to reconstruct fine details. To overcome this problem while maintaining the efficiency of the proposed module, we introduce Fused-MBConvs into the proposed network to model the local connectivity of different features. Experimental results demonstrate that the proposed ShuffleMixer is about 6x smaller than the state-of-the-art methods in terms of model parameters and FLOPs while achieving competitive performance. In NTIRE 2022, our primary method won the model complexity track of the Efficient Super-Resolution Challenge [23]. The code is available at https://github.com/sunny2109/MobileSR-NTIRE2022.
翻译:轻量度和效率是实际应用图像超分辨率算法的关键驱动因素。 我们提出一个简单而有效的方法,即ShuffleMixer,用于轻量图像超分辨率,探索大型混凝土和频道拆散操作。与以往的只是堆叠多个小内核变动或复杂操作者以学习演示的SR模型相比,我们探索一个大型内核ConvNet,用于移动友好型SR设计。具体地说,我们开发了一个大型深层次的ConvNet和两个投影层,其基础是频道分解和冲洗,作为高效混合功能的基本组成部分。由于自然图像的背景与当地密切相关,仅使用大深层混凝土不足以重建精细细节。为了在保持拟议模块效率的同时克服这一问题,我们将Fused-MBonvs引入拟议网络,以模拟不同功能的本地连通性。实验结果显示,拟议的Shifle20Mixer在模型参数和FLOPSOPs之间比州级方法要小6x。在取得具有竞争力的业绩时,自然图像环境环境环境环境环境环境环境环境环境环境数据系统系统第2022号。