ShuffleNet, an efficient neural network architecture, has gained prominence in computer vision for its high accuracy with low computational cost. However, its sensitivity to noise and perturbations limits robustness and feature representation. Existing methods like data augmentation and ensemble lack efficiency in noise reduction. This paper introduces MIR-ShuffleNet, enhancing ShuffleNet with regularized mutual information for noise reduction. MIR- ShuffleNet employs mutual information to enhance effective post-convolution features, reducing noise impact and redundancy. Extensive experiments on five datasets demonstrate MIR- ShuffleNet's efficiency and robustness superiority over existing ShuffleNet variants.