Simple, Accurate, and Robust Nonparametric Blind Super-Resolution
This work addresses the challenge of enhancing low-resolution images without prior knowledge of blur kernels, which is important for applications like photography and surveillance, but it appears incremental as it builds on existing methods.
The paper tackles the problem of single image nonparametric blind super-resolution by proposing an approach that minimizes a functional with a convolution consistency constraint and unnatural bi-l0-l2-norm regularization, achieving better performance than a recent method in kernel estimation accuracy and image quality.
This paper proposes a simple, accurate, and robust approach to single image nonparametric blind Super-Resolution (SR). This task is formulated as a functional to be minimized with respect to both an intermediate super-resolved image and a nonparametric blur-kernel. The proposed approach includes a convolution consistency constraint which uses a non-blind learning-based SR result to better guide the estimation process. Another key component is the unnatural bi-l0-l2-norm regularization imposed on the super-resolved, sharp image and the blur-kernel, which is shown to be quite beneficial for estimating the blur-kernel accurately. The numerical optimization is implemented by coupling the splitting augmented Lagrangian and the conjugate gradient (CG). Using the pre-estimated blur-kernel, we finally reconstruct the SR image by a very simple non-blind SR method that uses a natural image prior. The proposed approach is demonstrated to achieve better performance than the recent method by Michaeli and Irani [2] in both terms of the kernel estimation accuracy and image SR quality.