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We present a new procedure rooted in deep learning to construct scienceimages from data cubes collected by astronomical instruments using HxRGdetectors in low-flux regimes. It improves on the drawbacks of the conventionalalgorithms to construct 2D images from multiple readouts by using the readoutscheme of the detectors to reduce the impact of correlated readout noise. Wetrain a convolutional recurrent neural network on simulated astrophysicalscenes added to laboratory darks to estimate the flux on each pixel of scienceimages. This method achieves a reduction of the noise on constructed scienceimages when compared to standard flux-measurement schemes (correlated doublesampling, up-the-ramp sampling), which results in a reduction of the error onthe spectrum extracted from these science images. Over simulated data cubescreated in a low signal-to-noise ratio regime where this method could have thelargest impact, we find that the error on our constructed science images fallsfaster than a $1/\sqrt{N}$ decay, and that the spectrum extracted from theimages has, averaged over a test set of three images, a standard error reducedby a factor of 1.85 in comparison to the standard up-the-ramp pixel samplingscheme. The code used in this project is publicly available on GitHub

Correlated Read Noise Reduction in Infrared Arrays Using Deep Learning