A spatio-spectral hybridization for edge preservation and noisy image restoration via local parametric mixtures and Lagrangian relaxation

TL;DR

This paper presents an unsupervised, hybrid statistical method combining spatial decomposition and Fourier regularization to effectively restore and compress noisy images while preserving edges.

Contribution

It introduces a novel spatio-spectral hybrid approach using local parametric mixtures and Lagrangian relaxation for edge preservation and noise reduction.

Findings

Effective edge preservation demonstrated on noisy images

Improved image restoration quality over traditional Fourier methods

Robustness confirmed through experiments on degraded images

Abstract

This paper investigates a fully unsupervised statistical method for edge preserving image restoration and compression using a spatial decomposition scheme. Smoothed maximum likelihood is used for local estimation of edge pixels from mixture parametric models of local templates. For the complementary smooth part the traditional L2-variational problem is solved in the Fourier domain with Thin Plate Spline (TPS) regularization. It is well known that naive Fourier compression of the whole image fails to restore a piece-wise smooth noisy image satisfactorily due to Gibbs phenomenon. Images are interpreted as relative frequency histograms of samples from bi-variate densities where the sample sizes might be unknown. The set of discontinuities is assumed to be completely unsupervised Lebesgue-null, compact subset of the plane in the continuous formulation of the problem. Proposed spatial decomposition uses a widely used topological concept, partition of unity. The decision on edge pixel neighborhoods are made based on the multiple testing procedure of Holms. Statistical summary of the final output is decomposed into two layers of information extraction, one for the subset of edge pixels and the other for the smooth region. Robustness is also demonstrated by applying the technique on noisy degradation of clean images.