Non-contact diffuse optical tomography （DOT） is a novel technique exhibiting higher spatial detection density compared to traditional DOT. The dynamic range of the detector in non-contact DOT considerably influences the accuracy of the reconstruction of the absorption coefficient. In this study， the effect of low dynamic range of the detector on the reconstruction results was examined by conducting experiments and simulations. A method based on deep learning was proposed to extend the dynamic range of the detector. The training samples were generated in the NIRFAST software by setting different phantom absorption and scattering coefficients and incident light field parameters. A fully connected network was built for model training. The model was used to recover the detection data in the simulations and optical experiments. The data recovery and reconstruction results of the experiments indicate that the model recovers the error data of the detector with a low dynamic range while extending the dynamic range of the detector from 256 to 10⁹. The proposed method can effectively reduce the reconstruction error caused by the low dynamic range of the detector and provide an effective technical solution for non-contact DOT using detectors having a low dynamic range.