Wavelet Transforms

This function block applies wavelet-based transformation to a grayscale image to emphasize detail structures (high‑frequency content) and produce a processed grayscale output. It is useful for feature enhancement, edge highlighting, and preparing images for further analysis.

📥 Inputs

Input sockets

• Grayscale Image Provide a single-channel (gray/binary) image to be processed.

📤 Outputs

Output sockets

• Grayscale Image Processed image after wavelet transform and reconstruction.

🕹️ Controls

• Threshold Type Choose the wavelet family / transform type to apply. Different selections emphasize different detail characteristics.

• WaveletLevel Select the decomposition level (higher values emphasize coarser detail layers).

🎨 Features

• Adjustable wavelet family selection to change the style of detail extraction.

• Configurable decomposition level to control the scale of details that are emphasized.

• Outputs a single-channel image suitable for further processing (filtering, thresholding, detection).

⚙️ Running behavior

When the block runs it processes the input image according to the selected Threshold Type and WaveletLevel. The result is a reconstructed grayscale image where detail components are emphasized, ready for downstream blocks.

📝 Usage Instructions

1. Connect a grayscale image source to Grayscale Image input.

2. Select desired Threshold Type to match the texture/detail characteristics you want to emphasize.

3. Adjust WaveletLevel to control the detail scale (start low and increase to see effects).

4. Use the block output with visualization or analysis blocks to inspect or continue processing.

💡 Tips and Tricks

• If the input image is very large, use Image Resizer before this block to reduce processing time and memory usage.

• To reduce noise before transform, feed the image through Blur; this can produce cleaner detail emphasis.

• After wavelet processing, use Image Threshold or Image Adaptive Threshold to convert enhanced details into a binary form for detection tasks.

• Combine the output with Find Object or Histogram On Line to detect shapes or analyze line-based features on the enhanced image.

• Use Show Image to preview intermediate results quickly during tuning.

• Save examples or debug outputs with Image Logger or Image Write when building and testing a pipeline.

🛠️ Troubleshooting

• If the output looks overly noisy or fragmented, try reducing WaveletLevel or select a different Threshold Type.

• If no visible change occurs, ensure the input is a proper single-channel grayscale image and try increasing the WaveletLevel slightly.

• If results are too coarse, decrease WaveletLevel to emphasize finer details.