Targeted L1L2: Naturalness-constrained image recovery from random projections
We devise an elastic net convex program that generates natural-looking compressive sensed images by mimicking features indicative of good quality images. We use the shape parameter of divisively normalized wavelet coefficients to iteratively solve a convex program and target the distribution of each wavelet band. Using this method we are able to create better quality images than other methods as gauged by perceptually relevant quality indices (SSIM).