Scale sensitive deconvolution of interferometric images I. Adaptive Scale Pixel (Asp) decomposition

TL;DR

This paper introduces an adaptive scale pixel (Asp) deconvolution algorithm for interferometric images, emphasizing spatial correlation length as a key parameter for separating signal from noise, leading to improved residuals.

Contribution

It proposes a novel scale-sensitive deconvolution method using Asp modeling, enhancing noise separation in radio interferometric image processing.

Findings

Asp-based deconvolution yields more noise-like residuals.

Spatial correlation length is a stronger signal separator than signal strength.

Optimizations improve runtime performance.

Abstract

Deconvolution of the telescope Point Spread Function (PSF) is necessary for even moderate dynamic range imaging with interferometric telescopes. The process of deconvolution can be treated as a search for a model image such that the residual image is consistent with the noise model. For any search algorithm, a parameterized function representing the model such that it fundamentally separates signal from noise will give optimal results. In this paper, the first in a series of forthcoming papers, we argue that in general, spatial correlation length (a measure of the scale of emission) is a stronger separator of the signal from the noise, compared to the strength of the signal alone. Consequently scale sensitive deconvolution algorithms result into more noise-like residuals. We present a scale-sensitive deconvolution algorithm for radio interferometric images, which models the image as a collection of Adaptive Scale Pixels (Asp). Some attempts at optimizing the runtime performance are also presented.