# Simultaneous Registration of Image Sequences -- a novel singular value based images similarity measure

## TL;DR

The paper introduces the SqN method, a novel image similarity measure based on Schatten-q-norms, enabling faster and globally informed registration of image sequences compared to traditional local methods.

## Contribution

It proposes the SqN approach using Schatten-q-norms for global image sequence registration, improving speed and information integration over local methods.

## Key findings

- SqN achieves comparable registration accuracy to standard measures.
- SqN is approximately six times faster in computation.
- Global information transport improves registration quality.

## Abstract

The comparison of images is an important task in image processing. For a comparison of two images, a variety of measures has been suggested. However, applications such as dynamic imaging or serial sectioning provide a series of many images to be compared. When these images are to be registered, the standard approach is to sequentially align the j-th image with respect to its neighbours and sweep with respect to j. One of the disadvantages is that information is distributed only locally. We introduce an alternative so-called SqN approach. SqN is based on the Schatten-q-norm of the image sequence gradients, i.e. rank information of image gradients of the whole image sequence. With this approach, information is transported globally. Our experiments show that SqN gives at least comparable registration results to standard distance measures but its computation is about six times faster.