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A full-reference quality metric for analyzing restoration methods.

Project description

ERQA - Edge Restoration Quality Assessment

ERQA - a full-reference quality metric designed to analyze how good image and video restoration methods (SR, deblurring, denoising, etc) are restoring real details.

It is part of MSU Video Super Resolution Benchmark project.

Quick start

Run pip install erqa and run it from command line or directly from Python code.

Command line

python -m erqa /path/to/target.png /path/to/gt.png

Python code

import erqa
import cv2

# Target and gt should be uint8 arrays of equal shape (H, W, 3) in BGR format
target = cv2.imread('/path/to/target.png')
gt = cv2.imread('/path/to/gt.png')

metric = erqa.ERQA()
v = metric(target, gt)


The ERQA metric analyzes how details were reconstructed in an image compared to ground-truth.

  • ERQA = 1.0 means perfect restoration
  • ERQA = 0.0 means worst restoration

Visualization of the metric shows underlying mask showing where image is distorted.

  • Blue means there is a missing detail (False Negative)
  • Red means there is a misplaced detail (False Positive)
  • White means perfect details restoration (True Positive)
  • Black means perfect background restoration (True Negative)

Local setup

You can get source code up and running using following commands:

git clone
cd erqa
pip install -r requirements.txt

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