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arxiv: 2604.25558 · v1 · submitted 2026-04-28 · ⚛️ physics.ins-det

Recognition: unknown

Background Remover -- an effective tool for processing noisy microscopy images

Anna Kilian, Ma{\l}gorzata Sankowska, Pawe{\l} Bilski

Pith reviewed 2026-05-07 14:05 UTC · model grok-4.3

classification ⚛️ physics.ins-det
keywords background removerfluorescent microscopyImageJ pluginlow SNRimage denoisingsignal preservationnoise removalmicroscopy analysis
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The pith

The Background Remover plugin for ImageJ distinguishes signal pixels from noise in low signal-to-noise fluorescent microscopy images.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper introduces a software tool designed to clean up noisy images from fluorescent microscopy. It tackles the common problem of low signal-to-noise ratios and uneven backgrounds that make it hard to see and measure objects in such images. The core of the tool is an algorithm that keeps the real signal intact while getting rid of the noise pixels. This allows users to work with images where objects have different brightness levels and still get reliable results. The authors explain the method, show tests, and provide the program for free download.

Core claim

Background Remover (BGR) is a plugin for the ImageJ program that applies an algorithm to separate signal from noise in fluorescent microscopy images. By preserving signal pixels and removing noise, it supports analysis of objects with varying intensities even in low signal-to-noise conditions and can measure their intensities. The paper outlines the algorithm details, how the program functions, and results from performance tests.

What carries the argument

The signal-noise differentiation algorithm in Background Remover that classifies pixels to retain signal while eliminating background noise in heterogeneous images.

If this is right

  • Researchers can analyze microscopy images with objects of different intensities more effectively.
  • Identification of features becomes reliable even when signal-to-noise ratios are low.
  • The tool provides intensity values for the identified objects in the images.
  • Processing of fluorescent microscopy data with uneven backgrounds is improved.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • This method could be tested on images from other microscopy techniques like brightfield or electron microscopy.
  • Combining it with machine learning for further segmentation might increase its capabilities.
  • Performance on very complex background patterns could be explored in future studies.

Load-bearing premise

The algorithm can accurately identify signal pixels without mistaking noise for signal or removing parts of the true signal in varied background conditions.

What would settle it

Comparison of the tool's output on test images against manually verified signal regions or synthetic images with known signal and noise to measure accuracy, false positive rates, and signal preservation.

Figures

Figures reproduced from arXiv: 2604.25558 by Anna Kilian, Ma{\l}gorzata Sankowska, Pawe{\l} Bilski.

Figure 1
Figure 1. Figure 1: These small circular tracks also view at source ↗
Figure 2
Figure 2. Figure 2: Comparison of the performance of two standard ImageJ background subtraction procedures: (a) - ImageJ's built-in function "Subtract Background", (b) - Mosaic Suite's "Background Subtractor". The processed image is the cutout of view at source ↗
read the original abstract

Background Remover (BGR) is a novel software tool developed as a plugin to the well-known ImageJ program and designed to address the challenges of analysing fluorescent microscopy images characterized by low signal-to-noise ratios and heterogeneous backgrounds. The used algorithm effectively differentiates between signal and noise pixels, preserving the signal while eliminating noise. This functionality enables the analysis of images with objects of varying intensities, allowing for reliable identification even in low signal-to-noise ratio conditions. Furthermore, BGR offers the capability to determine the intensity of identified objects, enhancing its utility for researchers in the field. The paper describes the algorithm and the program functioning, as well as the carried out tests of its performance. The program is freely downloadable from the website https://kilianna.github.io/background-remover/

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces Background Remover (BGR), a novel ImageJ plugin for processing fluorescent microscopy images with low signal-to-noise ratios and heterogeneous backgrounds. The tool's algorithm is claimed to differentiate signal pixels from noise while preserving true signal, enabling reliable object identification and intensity measurement even for objects of varying intensities. The paper describes the algorithm, program functionality, and performance tests; the software is freely available for download.

Significance. If the effectiveness claims are substantiated, the tool would offer a practical, accessible aid for researchers analyzing noisy fluorescent microscopy data in physics and related fields, where low-SNR imaging is common. The open-source availability and integration with ImageJ are clear strengths that support reproducibility and adoption. However, the overall significance remains limited without quantitative evidence demonstrating advantages over existing background-subtraction methods.

major comments (2)
  1. [Algorithm description] Algorithm description section: the manuscript states that the algorithm 'effectively differentiates between signal and noise pixels' but provides no pseudocode, decision rule, statistical model, or equations detailing how this separation is performed (e.g., any adaptive thresholding, filtering, or intensity-based criterion). This omission prevents assessment of whether the method avoids artifacts or signal loss in heterogeneous backgrounds.
  2. [Performance tests] Performance tests section: the paper mentions that 'tests of its performance' were carried out, yet reports no concrete methodology, ground-truth datasets, evaluation metrics (precision, recall, intensity recovery error, or SNR gain), or direct comparisons to standard ImageJ background subtraction plugins. Without these, the central claim of reliable performance in low-SNR conditions lacks verifiable support.
minor comments (2)
  1. [Abstract] The abstract would benefit from a brief mention of key quantitative outcomes from the tests to strengthen the summary of results.
  2. Consider including example before/after images with scale bars and intensity profiles in a results figure to visually demonstrate the claimed signal preservation.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments and the positive view of the tool's potential utility. We address each major comment below and will revise the manuscript to incorporate the requested details.

read point-by-point responses

  1. Referee: [Algorithm description] Algorithm description section: the manuscript states that the algorithm 'effectively differentiates between signal and noise pixels' but provides no pseudocode, decision rule, statistical model, or equations detailing how this separation is performed (e.g., any adaptive thresholding, filtering, or intensity-based criterion). This omission prevents assessment of whether the method avoids artifacts or signal loss in heterogeneous backgrounds.

    Authors: We agree that the algorithm description in the current manuscript is primarily conceptual and lacks the technical specifics needed for full evaluation. The revised manuscript will include pseudocode for the core separation process, explicit decision rules (including any intensity-based or statistical criteria), and a description of how the method is designed to handle heterogeneous backgrounds while preserving signal. revision: yes

  2. Referee: [Performance tests] Performance tests section: the paper mentions that 'tests of its performance' were carried out, yet reports no concrete methodology, ground-truth datasets, evaluation metrics (precision, recall, intensity recovery error, or SNR gain), or direct comparisons to standard ImageJ background subtraction plugins. Without these, the central claim of reliable performance in low-SNR conditions lacks verifiable support.

    Authors: We acknowledge that the performance evaluation section requires substantial expansion to meet the standards for verifiable claims. The revised manuscript will detail the testing methodology, specify the ground-truth datasets employed, report quantitative metrics including precision, recall, intensity recovery error, and SNR gain, and include direct comparisons to existing ImageJ background-subtraction plugins. revision: yes

Circularity Check

0 steps flagged

No derivation chain present; paper is a software-tool description

full rationale

The manuscript describes the Background Remover (BGR) ImageJ plugin, its algorithm for differentiating signal from noise pixels, program functioning, and performance tests on fluorescent microscopy images. No equations, first-principles derivations, fitted parameters renamed as predictions, or uniqueness theorems are invoked. The central claim of effective signal preservation rests on described tests rather than any self-referential reduction or self-citation load-bearing step. This is a standard implementation-and-validation paper whose content is self-contained against external benchmarks and contains no circular steps by the enumerated patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The paper describes a software tool without introducing new mathematical axioms, free parameters, or invented entities. The contribution is algorithmic implementation and testing.

pith-pipeline@v0.9.0 · 5433 in / 1101 out tokens · 93028 ms · 2026-05-07T14:05:02.579270+00:00 · methodology

discussion (0)

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Reference graph

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