DVMSR: Distillated Vision Mamba for Efficient Super-Resolution

Xiaoyan Lei, Wenlong Zhang, Weifeng Cao; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 6536-6546

Efficient Image Super-Resolution (SR) aims to accelerate SR network inference by minimizing computational complexity and network parameters while preserving performance. Existing state-of-the-art Efficient Image Super-Resolution methods are based on convolutional neural networks. Few attempts have been made with Mamba to harness its long-range modeling capability and efficient computational complexity which have shown impressive performance on high-level vision tasks. In this paper we propose DVMSR a novel lightweight Image SR network that incorporates Vision Mamba and a distillation strategy. The network of DVMSR consists of three modules: feature extraction convolution multiple stacked Residual State Space Blocks (RSSBs) and a reconstruction module. Specifically the deep feature extraction module is composed of several residual state space blocks (RSSB) each of which has several Vision Mamba Moudles(ViMM) together with a residual connection. To achieve efficiency improvement while maintaining comparable performance we employ a distillation strategy to the vision Mamba network for superior performance. Specifically we leverage the rich representation knowledge of teacher network as additional supervision for the output of lightweight student networks. Extensive experiments have demonstrated that our proposed DVMSR can outperform state-of-the-art efficient SR methods in terms of model parameters while maintaining the performance of both PSNR and SSIM. The source code is available at https://github.com/nathan66666/DVMSR.git