Abstract: A neural network receives mono-color pixels in a Bayer pattern from an image sensor and interpolates pixels to generate full-color RGB pixels while also enlightening the image for better detail. A Back-Projection (BP) layer is added to each contracting layer in a U-net convolution neural network where feature depth is increased as pixels are downsampled, while a BP channel shrink layer is added to each expansion layer where feature depth is reduced to upsample and increase pixel resolution. Convolution layers in the BP layer lighten and darken images to generate an error that is then lightened and added to a weighted lightened input. The neural network learns the best weightings to correct for noise and error in the lightening process in these BP layers. Noise is reduced further by repeating a convolution and leaky Rectified Linear Unit (lrelu) in series for the first convolution in the BP layer that performs lightening.