Dataset quality
Blurry
Dataset quality
Blurry
Understand how blurriness impacts visual datasets and how to detect and address blurry images or objects using Visual Layer.
What Are Blurry Images and Objects?
Blurry images lack sharpness and clarity, often making it difficult to recognize objects or extract visual detail. This can significantly reduce the performance of machine learning models, lower data quality, and hinder accurate human annotation.
Common Causes of Blurriness
- Focus issues: Autofocus or manual focus failures can result in out-of-focus shots.
- Motion blur: Movement of the subject or camera during exposure leads to streaking or distortion.
- Camera shake: Handheld capture or slow shutter speeds often introduce unintended blur.
- Compression artifacts: Over-compressed images may lose edge detail and overall clarity.
Why It Matters
| Problem | Impact |
|---|---|
| Visual interpretation errors | Annotators may struggle to label blurry data, increasing labeling noise. |
| Model accuracy drop | Object detection, OCR, and segmentation models perform poorly on blurred inputs. |
| Poor user experience | Low-quality images degrade trust and engagement in user-facing applications. |
How to Handle Blurry Data with Visual Layer
Visual Layer detects blurry content automatically using quality filters. You can identify, isolate, or exclude blurry images and objects to maintain high dataset quality.
Recommended Actions
- Remove severe blur: For unusable images where critical information is lost, it’s best to exclude or discard them.
- Apply sharpening techniques: In borderline cases, you may attempt image restoration using filters or deblurring algorithms.
- Detect and filter in Visual Layer: Use issue filters in the UI to surface blurry content before training, annotation, or export.
Managing blurriness helps improve data clarity, annotation quality, and downstream model performance—especially in high-precision environments.