From Observations to Theoretical Consistency: Decoder Recovery in Coded Aperture Imaging Using Convolutional Neural Networks

Coded aperture imaging, crucial for low-light imaging in challenging conditions, requires specific decoders for image reconstruction. Traditional image reconstruction methods can be complex and focus on reconstruction rather than learning the underlying decoder. Our work introduces a one-layer CNN network for interpretable decoder recovery, without prior knowledge of encoding or decoding arrays. Using observed detector images, the network produces reconstructed images using a learned decoder. We train our network using the MNIST dataset and report high accuracy in image reconstruction even for images with high signal to noise ratio. To validate the generalizability of the method, we show that the MNIST-trained CNN-learned decoder is able to accurately reconstruct images from the grayscale FashionMNIST dataset.