Boundary length of reconstructions in discrete tomography

TL;DR

This paper establishes a new lower bound on the boundary length of binary image reconstructions in discrete tomography, effectively combining row and column sum information for improved bounds.

Contribution

It introduces a novel lower bound on boundary length that integrates both row and column sum data, advancing previous simple bounds.

Findings

New lower bound on boundary length derived

Bound effectively combines row and column sum information

Improves upon previous simple bounds

Abstract

We consider possible reconstructions of a binary image of which the row and column sums are given. For any reconstruction we can define the length of the boundary of the image. In this paper we prove a new lower bound on the length of this boundary. In contrast to simple bounds that have been derived previously, in this new lower bound the information of both row and column sums is combined.