  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
