Model-based adaptive resolution upconversion of degraded images

Resolution upconversion of a degraded image is an ill-posed inverse problem that is even harder than video superresolution due to lack of redundant observations from reference frames. To overcome this difficulty an adaptive 2D piecewise autoregressive (PAR) model is used to strengthen the constraints on the solution of the inverse problem. The PAR model can be fit to local image waveforms by adjusting its parameters. But estimating the model parameters needs the knowledge of the very original high-resolution pixels to be estimated by the model. We resolve this chicken-and-egg dilemma by adaptive nonlinear least-squares joint estimation of both model parameters and original pixels. This non-linear estimation problem is solved by the method of structured total least-squares, constrained by the degradation function (e.g., the point spread function of a camera plus noises) that forms the observed low-resolution image. As such, this work offers a unified general framework for joint upsampling, deconvolution, and denoising. Moreover, the upsampling can be carried out at an arbitrary scale rather than power of two. Experiments show that the proposed NEARU technique outperforms current methods in both PSNR and subjective visual quality, and its advantage becomes greater for larger scaling factors.