English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

One representation of possible errors in a grayscale image reconstruction isas another grayscale image estimating potentially worrisome differences betweenthe reconstruction and the actual "ground-truth" reality. Visualizations andsummary statistics can aid in the interpretation of such a representation oferror estimates. Visualizations include suitable colorizations of thereconstruction, as well as the obvious "correction" of the reconstruction bysubtracting off the error estimates. The canonical summary statistic would bethe root-mean-square of the error estimates. Numerical examples involvingcranial magnetic-resonance imaging clarify the relative merits of the variousmethods in the context of compressed sensing. Unfortunately, the colorizationsappear likely to be too distracting for actual clinical practice, and theroot-mean-square gets swamped by background noise in the error estimates.Fortunately, straightforward displays of the error estimates and of the"corrected" reconstruction are illuminating, and the root-mean-square improvesgreatly after mild blurring of the error estimates; the blurring is barelyperceptible to the human eye yet smooths away background noise that wouldotherwise overwhelm the root-mean-square.

Methods of interpreting error estimates for grayscale image reconstructions