Image Binarization Using Adaptive Threshold Method

Implement image binarization using the adaptive threshold method. The process begins by dividing the input image into multiple sub-images or blocks. For each sub-image, compute the mean pixel intensity value, which serves as the local threshold for binarizing that specific region.

Within each sub-image, compare individual pixel values against the calculated mean threshold. Pixels with intensity values greater than the mean are set to white (typically value 255), while pixels below the threshold are set to black (value 0). This local thresholding approach allows for better handling of varying lighting conditions across the image.

This block-based adaptive thresholding technique enables effective separation of foreground objects from background by accounting for local intensity variations. The method is particularly useful for images with uneven illumination or varying contrast, as it processes different image regions independently using their respective statistical characteristics.