An Improved Otsu's Method for Image Thresholding

Otsu's Method is a classic automatic thresholding algorithm in image processing that determines the optimal segmentation threshold by maximizing inter-class variance. To address the limitations of traditional Otsu's method, researchers have proposed several improvement directions:

For low-contrast images, improved algorithms typically adopt multi-level thresholding strategies, dividing the grayscale histogram into multiple regions and calculating local thresholds separately. This can be implemented using recursive thresholding or multi-Otsu methods that partition the histogram iteratively. Another approach incorporates neighborhood information by combining pixel spatial relationships to enhance noise resistance, often implemented through adaptive window-based calculations or morphological filtering.

Some enhanced versions preprocess images first, such as applying histogram equalization to enhance contrast or using morphological operations to smooth noise. Code implementations often include preprocessing stages using OpenCV functions like cv2.equalizeHist() and cv2.morphologyEx(). Other methods integrate Otsu's method with genetic algorithms, improving threshold selection efficiency through optimized search techniques that use fitness functions based on inter-class variance.

The most advanced improvement direction involves adaptive Otsu's methods that dynamically adjust thresholds according to local image characteristics. These algorithms are particularly suitable for processing document images with uneven illumination or medical images, significantly improving segmentation accuracy while maintaining algorithmic simplicity. Implementation typically involves sliding window techniques or region-growing approaches that calculate thresholds for local image patches.