This research integrates learning mechanisms of weights calibration and multiple receptive fields in SENet into UNet, working out USENet to achieve super-resolution in digital holographic phase cell images. Structured with symmetrical topology and filtered with multi-scale, the model is trained to improve the image rebuilding accuracy and the capability of generalization. So as to enhance the estimation confidence in the region of interest, the weights calibration blocks are introduced to differentiate the importance of feature map channels. With better visual effects and details, the experimental results confirm that the average numerical score of structural similarity index on validation set has been verified to improve from 0.770 2 to 0.942 7. According to the performances between experimental group of USENet and reference group of network without calibration layer, the region of interests in global images have demonstrated a further improvement from 0.965 5 to 0.970 3 by the block of calibration.