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Abstract: Due to the scattering and absorption of light caused by the suspended particles in the water body and the turbulence of high-frequency random motion, the underwater image has a series of problems such as blurred texture, low resolution and distortion. However, most of the existing deep learning image super-resolution reconstruction algorithms have the problems of complex computation, large model complexity and high memory occupation. To solve this problem, a lightweight underwater image super-resolution reconstruction network based on blueprint separable convolution is proposed. The model is divided into four stages: shallow feature extraction, deep feature extraction, multi-layer feature fusion and image reconstruction, In the depth feature extraction stage, feature distillation branches are removed on the basis of BSRN and the number of channels is increased for compensation. At the same time, the three blueprints convolution is used to carry out residual local feature learning to simplify feature aggregation and realize the lightweight of the network. The experimental results show that the proposed method is superior to the currently proposed hyperspectral algorithm in terms of running time, parameter quantity and model complexity. When the amplification factor is 2 and 4, the peak signal-to-noise ratio (PSNR) and structure similarity (SSIM) average values reach 31.5560dB, 0.8620 and 27.7088dB, 0.7213 respectively, and the reconstruction quality is further improved.