Dr. Farshid Pirahansiah
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By Dr. Farshid Pirahansiah — computer vision & embedded edge AI consultant, Berlin, Germany · 12+ years. Updated 2026-09-19.

PSNR-based thresholding method for image segmentation in automated visual inspection.

PEAK_SIGNAL-TO-NOISE_RATIO_BASED_ON_THRESHOLD_METHOD_FOR_IMAGE_SEGMENTATION

https://www.pirahansiah.com/notes/pubs/journals/PEAK_SIGNAL-TO-NOISE_RATIO_BASED_ON_THRESHOLD_METHOD_FOR_IMAGE_SEGMENTATION/

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PEAK SIGNAL-TO-NOISE RATIO BASED ON THRESHOLD METHOD FOR IMAGE SEGMENTATION

PEAK SIGNAL-TO-NOISE RATIO BASED ON THRESHOLD METHOD FOR IMAGE SEGMENTATION

Mind Map: Peak Signal-to-Noise Ratio Based on Threshold Method for Image Segmentation

1. Introduction

  • Importance of Thresholding: Separates objects from the background, crucial in image processing and pattern recognition.
  • Types of Thresholding:
  • Single Thresholding: Produces binary images (0 and 1).
  • Multilevel Thresholding: Produces images with pixel values between 0 and 255.
  • Objective: Develop a new algorithm using Peak Signal-to-Noise Ratio (PSNR) for image segmentation.

2. Thresholding Techniques

2.1 Single Thresholding

  • Definition: Uses a single threshold value to convert the image into binary.
  • Goal: Maximize segmentation accuracy and reduce storage requirements.
  • Methods:
  • Kittler and Illingworth’s MET.
  • Potential Difference.

2.2 Multilevel Thresholding

  • Definition: Uses more than one threshold value to segment images.
  • Goal: Applied when a single threshold is insufficient for global segmentation.
  • Method:
  • Arora’s Recursive Algorithm.

2.3 Multi-Thresholding

  • Definition: Applies multiple threshold values, calculates blobs/objects in the image.
  • Goal: Selects peak threshold values based on object count.
  • Method: Abdullah’s Multi-Threshold Algorithm.

2.4 Otsu’s Method

  • Definition: Unsupervised, nonparametric method for automatic threshold selection.
  • Goal: Maximizes the variance between classes to select optimal thresholds.

3. PSNR-Based Thresholding

  • Proposed Method: Utilizes PSNR as a measure to select the optimal threshold.

  • Formula:PSNR = 10 * log10(MAX^2 / MSE) where MSE is the mean squared error and MAX is the maximum possible pixel value.

  • Algorithm: 1. Calculate PSNR for each threshold value. 2. Select the threshold that maximizes PSNR.

4. Results and Evaluation

  • Datasets:
  • DIBCO 2011 (handwritten and printed images).
  • Metrics:
  • F-Measure: Evaluates binary classification accuracy.
  • PSNR: Measures similarity between the original and thresholded images.

4.1 Performance

  • The proposed method showed better PSNR and acceptable F-measure for printed images.
  • Comparison with Kittler and Illingworth, Otsu, and other methods highlighted superior performance of the proposed method in certain conditions.

5. Application Areas

  • Automated Visual Inspection.
  • License Plate Recognition.
  • Handwritten Image Segmentation.
  • Image Processing in Mobile Devices (with resource limitations).

6. Conclusion

  • Proposed Method: Adaptive PSNR-based thresholding is a viable approach for image segmentation.
  • Results: Competitive performance in standard, printed, and handwritten images.
  • Future Work: Potential for further improvement, especially in handling complex images with varying contrast.

Source: Pirahansiah, F. “PSNR Threshold for Image Segmentation”. pirahansiah.com, updated 2026-09-19. Canonical URL https://pirahansiah.com/farshid/content/journals-psnr-segmentation/ · markdown source journals-psnr-segmentation.md · how to cite, quote and link this site.