> For the complete documentation index, see [llms.txt](https://docs.augelab.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.augelab.com/function-blocks/blocks-reference/image-transformations/transformation-filters/wavelet-transforms.md).

# 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 <a href="#inputs" id="inputs"></a>

Input sockets

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

## 📤 Outputs <a href="#outputs" id="outputs"></a>

Output sockets

* `Grayscale Image` Processed image after wavelet transform and reconstruction.

## 🕹️ Controls <a href="#controls" id="controls"></a>

* `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 <a href="#features" id="features"></a>

* 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 <a href="#how-it-works" id="how-it-works"></a>

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 <a href="#usage" id="usage"></a>

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 <a href="#tips-and-tricks" id="tips-and-tricks"></a>

* 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 <a href="#troubleshooting" id="troubleshooting"></a>

* 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.