Efficient $\ell^0$ gradient-based Super Resolution for simplified image segmentation

TL;DR

This paper introduces an efficient $ ext{L}^0$ gradient-based super-resolution model using a novel ADMM algorithm, demonstrating improved image quality and segmentation accuracy over existing methods on synthetic and real data.

Contribution

It proposes a new $ ext{L}^0$ gradient regularization model with an ADMM solver for super-resolution, enhancing segmentation tasks in degraded images.

Findings

Super-resolved images improve segmentation accuracy in QR and cell detection.

The $ ext{L}^0$ regularization outperforms other variational and deep-learning methods.

The proposed algorithm is computationally efficient and effective on real-world data.

Abstract

We consider a variational model for single-image super-resolution based on the assumption that the gradient of the target image is sparse. We enforce this assumption by considering both an isotropic and an anisotropic $\ell^0$ regularisation on the image gradient combined with a quadratic data fidelity, similarly as studied in [1] for general signal recovery problems. For the numerical realisation of the model, we propose a novel efficient ADMM splitting algorithm whose substeps solutions are computed efficiently by means of hard-thresholding and standard conjugate-gradient solvers. We test our model on highly-degraded synthetic and real-world data and quantitatively compare our results with several variational approaches as well as with state-of-the-art deep-learning techniques. Our experiments show that $\ell^0$ gradient-regularised super-resolved images can be effectively used to improve the accuracy of standard segmentation algorithms when applied to QR and cell detection, and landcover classification problems, in comparison to the results achieved by other approaches.