Optical coherence tomography (OCT) is widely used in ophthalmic diagnosis and adjuvant therapy, but its imaging quality is inevitably affected by speckle noise and motion artifacts. This article proposes a multi teacher knowledge distillation network MK-OCT for OCT super-resolution tasks, which uses teacher networks with different advantages to train balanced, lightweight, and efficient student networks. The use of efficient channel distillation method ECD in MK-OCT also enables the model to better preserve the texture information of retinal images, meeting clinical needs. The experimental results show that compared with classical super-resolution networks, the model proposed in this paper performs well in both reconstruction accuracy and perceptual quality, with smaller model size and less computational complexity.