Aiming at the problem of edge distortion and fuzzy texture detail information in reconstructed images, an image super-resolution reconstruction method based on improved Convolutional Neural Network (CNN) was proposed. Firstly, various preprocessing operations were performed on the underlying feature extraction layer by three interpolation methods and five sharpening methods, and the images which were only subjected to one interpolation operation and the images which were sharpened after interpolation operation were arranged into a 3D matrix. Then, the 3D feature map formed by the preprocessing was used as the multi-channel input of a deep residual network in the nonlinear mapping layer to obtain deeper texture detail information. Finally, for reducing image reconstruction time, sub-pixel convolution was introduced into the reconstruction layer to complete image reconstruction operation. Experimental results on several common datasets show that the proposed method achieves better restored texture detail information and high-frequency information in the reconstructed image compared with the classical methods. Furthermore, the Peak Signal-to-Noise Ratio (PSNR) was increased by 0.23 dB on average, and the structural similarity was increased by 0.0066 on average. The proposed method can better maintain the texture details of the reconstructed image and reduce the image edge distortion under the premise of ensuring the image reconstruction time, improving the performance of image reconstruction.