Iterative specularity factorization for image enhancement

A system for enhancing light in an image using a recursive factorization network and a fusion network, the system comprising:

• a processor;
• and
• a memory storing instructions that, when executed by the processor, configure the system to:
• receive, by the recursive factorization network, an input image as a first input from an image-capturing device;
• decompose, via the recursive factorization network comprising a sequence of K iterative factorizations, the input image into a plurality of K additive factors by:
• a. performing an optimization process, at a first K iteration, to apply an unrolled L1 optimization on the first input over T inner iterations, to estimate a first additive factor comprising an image specularity or highlights as matrix sparsity using learned parameters that are specific to the first K iteration;
• b. computing a second input by subtracting the first input from the first additive factor, wherein the second input corresponds to the input image with a reduced sparsity constraint;
• c. performing the optimization process, at a second K iteration, to apply the unrolled L1 optimization on the second input over T inner iterations, to estimate a second additive factor comprising an image specularity or highlights as matrix sparsity using learned parameters that are specific to the second K iteration;
• and
• d. progressively reducing the sparsity constraint associated with the input image for each consecutive iteration to enable the extraction of increasingly less sparse additive factors;
• process the plurality of K additive factors using the fusion network to generate a plurality of enhancement maps configured to adjust pixel intensities in the input image;
• and
• generate an enhanced output image by applying the plurality of enhancement maps to the input image using a differentiable bilateral filtering layer for smoothness and artifact reduction.