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New image quality gate for vision pipelines achieves high accuracy

By PulseAugur Editorial · [2 sources] ·

Researchers have developed MagikaDocumentFromPixel, a fast and efficient image quality gate for vision pipelines. This system can classify images as sharp, blurred, or uncertain in approximately 7 milliseconds on a single CPU core. The method utilizes an Edge Prior Module (EPM) and is trained on high-resolution images, achieving a high F1 score and AUC. AI

IMPACT This image quality gate could improve the efficiency of vision pipelines by filtering out unusable blurry images before they are processed by more computationally intensive models.

RANK_REASON The cluster contains two identical arXiv preprints detailing a new research paper on a vision-language pipeline component.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New image quality gate for vision pipelines achieves high accuracy

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The cluster contains two identical arXiv preprints detailing a new research paper on a vision-language pipeline component.
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94 days old
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Duy Tran Thanh ·

    Edges Before Embeddings: A Confidence-Aware Blur Gate for Vision-Language Pipelines

    Production vision pipelines silently degrade on blurry input, wasting compute on downstream OCR, retrieval, and vision-language model (VLM) calls that cannot recover a usable output. We present MagikaDocumentFromPixel, a lightweight, CPU-friendly image quality gate that classifie…

  2. arXiv cs.CV TIER_1 English(EN) · Duy Tran Thanh ·

    Edges Before Embeddings: A Confidence-Aware Blur Gate for Vision-Language Pipelines

    arXiv:2606.25838v1 Announce Type: new Abstract: Production vision pipelines silently degrade on blurry input, wasting compute on downstream OCR, retrieval, and vision-language model (VLM) calls that cannot recover a usable output. We present MagikaDocumentFromPixel, a lightweight…

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