Filtering-out poor-quality images for data preparation

Gour Karmakar; Muhammad Imran; Roopdeep Kaur

Filtering-out poor-quality images for data preparation

T0 review · 4 major / 3 minor · reviewed 2026-07-15 · grok-4.5

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Pith's one-line read Filtering out low-quality images beats denoising for recognition accuracy on traffic and object data.

desk verdict Abstract-only empirical recipe for IQA-based discard vs denoising; useful idea, but threshold selection and metric identity are unchecked so the accuracy claims cannot be treated as settled evidence. read the letter →

arxiv 2607.12352 v1 pith:QDBQLSUW submitted 2026-07-14 cs.CV

Roopdeep Kaur , Gour Karmakar , Muhammad Imran This is my paper · ORCID

classification cs.CV

keywords imagequalityassessmentdatapreparationfilteringdeeplearningtrafficsignrecognitionobjectnoisehandlingautonomousvehicles

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper argues that the usual first step of cleaning noisy images for deep learning is the wrong move. Instead of applying median, Gaussian, bilateral, or CNN denoisers that only handle some kinds of environmental and camera noise and force resizing, the authors filter out poor-quality images entirely. Quality is scored with an image-quality metric; an optimum threshold drops the bad ones while still leaving enough samples to train a model. On real and simulated traffic-sign and object-recognition data the approach yields higher average recognition accuracy than state-of-the-art denoisers (93.8 % and 84.9 % respectively). If the claim holds, practitioners preparing vision datasets for autonomous vehicles and similar systems can simply discard rather than repair, gaining both accuracy and simplicity.

What carries the argument

An image-quality-assessment (IQA) metric paired with a single optimum threshold that discards low-scoring images yet keeps a sufficient training set for the downstream deep-learning model.

What would settle it

Re-run the identical recognition pipelines on the same traffic-sign and object datasets after selecting the quality threshold only on a held-out validation split, then compare accuracy against the same denoisers; if the filtered approach no longer outperforms, the claim fails.

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Extended reading notes

Core claim

Filtering out poor-quality images with an image-quality-assessment metric and an optimum threshold, while retaining enough images for deep-learning training, produces higher recognition accuracy than state-of-the-art denoising methods across diverse environmental and camera noise.

Load-bearing premise

That a single (or per-dataset) optimum threshold on the quality metric can be chosen so that the remaining images both suffice for training and deliver fair, generalizable gains over denoising without being tuned to the same evaluation that reports the accuracy numbers.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

This is an abstract-only cs.CV data-prep paper. The one thing to know is that the load-bearing claim is empirical and currently unverifiable: discard images below an “optimum” IQA threshold (while keeping enough for DL training) and you beat SOTA denoising on traffic-sign and object recognition (93.8% and 84.9% average accuracy). Filtering bad samples is not a new principle—standard hygiene—but the concrete package (IQA + threshold as a substitute for denoising on these domains, with those numbers) is what they are selling, and the “for the first time” framing is overstated relative to common practice.

What they do well, on the face of it, is state a clear practical alternative and report point results on real and simulated traffic/object data with an eye toward AV-style use. If the full paper later names a fixed metric, a held-out or transferable threshold rule, baselines, and enough retained data under diverse noise, that would be a legitimate engineering contribution worth citing for data-prep pipelines.

The soft spots are real and proportional to what we can see. The free parameter is the optimum quality threshold. The abstract does not say which IQA metric, how the threshold is chosen, whether it is tuned on the same accuracy numbers, or how much data remains under different noise regimes. That is the circularity risk the stress-test flags, and it lands: without that protocol the reported gains could partly be “easier remaining examples.” No error bars, ablations, or named baselines either. Soundness and generalizability are therefore open, not refuted.

Who it is for: people building recognition under environmental/camera noise who care about prep recipes more than theory. A serious referee should see the full methods if the journal wants applied CV work; I would not desk-reject solely on the abstract, but I would not cite or bring it to reading group until the threshold story and metric are fixed and checkable. Treat the percentages as claims, not evidence, until then.

Referee Report

4 major / 3 minor

Summary. The manuscript (available only as an abstract) proposes replacing conventional image denoising with a discard-based data-preparation step: images are scored by an (unnamed) image quality assessment (IQA) metric, those below an “optimum threshold” are removed, and the retained set is required to remain large enough to train a deep recognition model. On real and simulated traffic-sign and object-recognition data the authors report average recognition accuracies of 93.8% and 84.9%, respectively, claimed to exceed state-of-the-art denoising baselines, with intended application to autonomous driving.

Significance. If the filtering pipeline were shown to be metric-specified, threshold-validated on held-out data, and superior to named denoising baselines under controlled discard rates, the result would be a simple, practically useful alternative to denoising for recognition pipelines that can tolerate reduced sample size. The abstract’s emphasis on retaining enough data for DL training and on real-life AV use cases is therefore potentially consequential. Because the full methods, baselines, and validation protocol are not supplied, that significance cannot yet be credited as demonstrated.

major comments (4)

[Abstract] The central control is an “optimum threshold” on an IQA score. The abstract neither names the IQA metric nor states how the threshold is chosen (a priori, held-out validation, or tuned to the same recognition accuracy later reported). Without that procedure the headline gains (93.8%, 84.9%) cannot be distinguished from post-hoc cutoff fitting on the evaluation metric, which is the load-bearing circularity risk for the claim that filtering outperforms denoising.
[Abstract] The claim of “performance supremacy … compared with the state-of-the-art approaches” is unsupported in the provided text: no denoising baselines are named, no train/val/test protocol or split is given, and no error bars, significance tests, or discard-rate / retained-N figures are reported. The two point accuracies therefore cannot be treated as evidence that filtering generalizes better than denoising across environmental and camera noise.
[Abstract] The abstract asserts that “a sufficient number of images remain to develop the deep learning (DL) model” but supplies no retained-set sizes, class-balance checks, or ablation of accuracy versus discard rate. Without those quantities it is impossible to verify that the retained subset is both large enough and representative rather than an easier residual of high-quality examples.
[Abstract (scope of review)] Only the abstract was available for review; methods, equations, tables, figures, and code are absent. Under these conditions the empirical central claim cannot be verified or falsified, so a definitive accept/reject decision on the full manuscript is not possible from the material provided.

minor comments (3)

[Abstract] The phrase “for the first time” is a strong novelty claim that should be backed by a brief related-work contrast once the full text is available; as written it is unsubstantiated.
[Abstract] “Real and simulated traffic and object recognition data” should name the datasets (or describe the simulation) so that the reported percentages can be contextualized against published numbers.
[Abstract] The abstract conflates “filtering noise” (denoising) with “filtering out poor-quality images” (sample rejection); a clearer terminological distinction would reduce reader confusion.

Circularity Check

0 steps flagged · score 0.0 of 10

Abstract-only review: no derivation chain or threshold-selection procedure is available to inspect, so circularity cannot be established from the paper's own text.

full rationale

Only the abstract is available; the full methods, equations, IQA metric identity, and threshold-selection procedure are not present. Circularity analysis requires quoting the paper and exhibiting a specific reduction (e.g., fitted parameter renamed as prediction, or X defined in terms of Y). The abstract states that quality is assessed with an IQA metric and an 'optimum threshold' is used to filter poor-quality images while retaining enough data for DL training, and reports higher recognition accuracy than SOTA denoising. It does not define how the threshold is chosen, whether it is tuned on the same accuracy metric, or any equations that would make the reported gains tautological by construction. Under the hard rules, absence of methods cannot be converted into a circularity finding by speculation; the honest outcome is score 0 with empty steps. Concerns about possible post-hoc threshold tuning or generalizability are correctness/evaluation risks, not demonstrated circularity from the paper's text.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

From the abstract alone the claim rests on an unspecified IQA metric, a free 'optimum threshold,' and the domain premise that quality scores track downstream recognition better than denoising. No new physical entities; the free parameter and domain assumptions carry the result.

free parameters (1)

optimum_quality_threshold
Abstract states an 'optimum threshold' is used to filter poor-quality images; value and selection procedure are not given and almost certainly tuned to keep accuracy high while retaining enough samples.

assumptions (3)

domain assumption An image quality assessment metric score is a reliable proxy for suitability of an image for downstream recognition training under environmental and camera noise.
The entire filter-out pipeline depends on IQA ranking matching task utility; not derived in the abstract.
domain assumption After discarding images below the threshold, the remaining set is still large and diverse enough to train a competitive deep recognition model.
Stated as a design constraint ('we also ensure that a sufficient number of images remain') but not proven; if false, the method fails.
domain assumption Standard comparison of recognition accuracy against median/Gaussian/bilateral and CNN denoisers is a fair evaluation of data-prep quality.
Implicit evaluation axiom; fairness depends on matched compute, same backbone, and held-out noise conditions not specified here.

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Cite this review

Pith. "Pith review of Filtering-out poor-quality images for data preparation." pith.science (2026). https://pith.science/paper/QDBQLSUW

@misc{pith2026260712352,
  author       = {Pith},
  title        = {Pith review of: Filtering-out poor-quality images for data preparation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QDBQLSUW}},
  note         = {Machine review of arXiv:2607.12352}
}

read the original abstract

Filtering noise is a fundamental part of data preparation that enhances image quality for applications such as object segmentation, detection, and recognition. Various noise reduction techniques are proposed in the literature, including the use of median, Gaussian, and bilateral filters. Convolutional neural networks (CNNs) have gained popularity in image denoising owing to their ability to extract complex patterns and features from data. CNNs are highly adaptable, making them effective tools for various image-denoising tasks. One drawback of CNN-based techniques is that they require an appropriate training dataset and all images to be resized. Another notable drawback of all these filtering techniques is that they work for certain types of environmental and camera noises. To bridge this research gap, in this paper, for the first time, instead of denoising, we propose an approach that filters out poor-quality images for various environmental and camera impacts. In our approach, quality is assessed using an image quality assessment metric and an optimum threshold is used to filter out poor-quality images. We also ensure that a sufficient number of images remain to develop the deep learning (DL) model. The results produced using real and simulated traffic and object recognition data demonstrate the performance supremacy of the proposed approach compared with the state-of-the-art approaches. The average recognition accuracy for our proposed approach is 93.8% for the traffic sign recognition dataset and 84.9% for the object recognition dataset. This indicates our model's potential for real-life applications such as autonomous vehicles.

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