Two-step training deep learning framework for computational imaging without physics priors

Abstract

Deep learning (DL) is a powerful tool in computational imaging for many applications. A common strategy is to use a preprocessor to reconstruct a preliminary image as the input to a neural network to achieve an optimized image. Usually, the preprocessor incorporates knowledge of the physics priors in the imaging model. One outstanding challenge, however, is errors that arise from imperfections in the assumed model. Model mismatches degrade the quality of the preliminary image and therefore affect the DL predictions. Another main challenge is that many imaging inverse problems are ill-posed and the networks are over-parameterized; DL networks have flexibility to extract features from the data that are not directly related to the imaging model. This can lead to suboptimal training and poorer image reconstruction results. To solve these challenges, a two-step training DL (TST-DL) framework is proposed for computational imaging without physics priors. First, a single fully-connected layer (FCL) is trained to directly learn the inverse model with the raw measurement data as the inputs and the images as the outputs. Then, this pre-trained FCL is fixed and concatenated with an un-trained deep convolutional network with a U-Net architecture for a second-step training to optimize the output image. This approach has the advantage that does not rely on an accurate representation of the imaging physics since the first-step training directly learns the inverse model. Furthermore, the TST-DL approach mitigates network over-parameterization by separately training the FCL and U-Net. We demonstrate this framework using a linear single-pixel camera imaging model. The results are quantitatively compared with those from other frameworks. The TST-DL approach is shown to perform comparable to approaches which incorporate perfect knowledge of the imaging model, to be robust to noise and model ill-posedness, and to be more robust to model mismatch than approaches which incorporate imperfect knowledge of the imaging model. Furthermore, TST-DL yields better results than end-to-end training while suffering from less overfitting. Overall, this TST-DL framework is a flexible approach for image reconstruction without physics priors, applicable to diverse computational imaging systems.