P3d Debinarizer Info

model = P3DModel( binarization_levels=8, # 8 bits per dimension spatial_smoothness=0.1, # Correlation across x,y temporal_memory=5, # Look back 5 time steps output_type="probability_field" # or "point_estimate" )

If you want to implement a lightweight p3d debinarizer in Python, follow this architectural blueprint.

Input:  binary_samples[0..N-1] at 2 GSPS
Output: PDW struct array
for each rising edge at index i:
refine_TOA = interpolate(binary_samples[i-1], binary_samples[i])
find next falling edge at index j
refine_TOA_fall = interpolate(binary_samples[j-1], binary_samples[j])
PW = refine_TOA_fall - refine_TOA
if PW_min < PW < PW_max:
RF = channel_estimate(i, j)  // from channelizer output
append PDW(TOA=refine_TOA, RF, PW)
 p3d debinarizer

A baseline P3D-inspired approach uses the Euclidean distance transform to create a height map from the binary edges. model = P3DModel( binarization_levels=8, # 8 bits per

# Distance transform from the binary edges
dist_transform = cv2.distanceTransform(binary_mask, cv2.DIST_L2, 5)
# Normalize to 0-255
debinarized_distance = cv2.normalize(dist_transform, None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8)
plt.imshow(debinarized_distance, cmap='gray')
plt.title('Distance Transform Debinarizer')
plt.show()

This method works surprisingly well for shapes with smooth gradients but fails for textures.