Removing photography artifacts using gradient projection and flash-exposure sampling

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Exposure Fusion (2009, methodology): “Previous techniques for this are much more elaborate [9, 2] and are specifi-”
Single Image Reflection Separation with Perceptual Losses (2018, background): “Some other multi-image approaches include the use of flash and noflash image pairs to improve the flash image with reflection removed [1].”
LLNet: A deep autoencoder approach to natural low-light image enhancement (2015, methodology): “We approach the problem of low-light image enhancement from a representation learning perspective us-ing deep autoencoders (we refer to as Low-light Net, LL-Net) trained to learn underlying signal features in low-light images and adaptively brighten and denoise.”
A Generic Deep Architecture for Single Image Reflection Removal and Image Smoothing (2017, background): “Obviously this is an ill-posed problem as there exist infinite feasible solutions, and hence most reflection removal algorithms require multiple input images [7, 30, 1, 19, 12, 22, 40, 41] or manual user interactions [21] to label reflectionand background-layer gradients, thus condensing the space o”
What Is the Range of Surface Reconstructions from a Gradient Field? (2006, background): “Reconstruction from gradient fields is important in several applications such as photometric stereo (PS) and shape from shading (SfS) [1], mesh smoothing, retinex [2], high dynamic range compression [3], phase unwrapping, image editing, matting and fusion [4].”