Bilateral Filter

This function block applies a bilateral filter to the input image. The bilateral filter is a powerful technique for smoothing images while preserving edges, making it beneficial for reducing noise without losing important details.

📥 Inputs

Image Any The input image that you want to apply the bilateral filter to.

📤 Outputs

Image Any The output image that has been processed with the bilateral filter.

🕹️ Controls

Kernel Size A slider that allows you to adjust the size of the kernel used in the filter. Larger values will result in a smoother image, but may also cause loss of detail.

Sigma Value A slider that defines the filter's spatial extent and influences how much weight is given to the neighboring pixels. Higher values will consider more distant pixels in the averaging process.

🎨 Features

Customizable Filtering Users can easily manipulate the kernel size and sigma value to achieve the desired level of smoothing.

Edge Preservation The bilateral filter effectively reduces noise while keeping edges sharp, making it suitable for various image processing tasks.

📝 Usage Instructions

1. Connect Input Image: Link any image you wish to smooth to the input of this block.

2. Adjust Kernel Size: Use the Kernel Size slider to set the size of the kernel—this should typically be an odd number.

3. Set Sigma Value: Use the Sigma Value slider to adjust how strongly the filter impacts the image.

4. Evaluate: Run the block to apply the bilateral filter to your image.

📊 Evaluation

When executed, this function block processes the input image and outputs it with the bilateral filter applied, reducing noise while maintaining the sharpness of edges.

💡 Tips and Tricks

Kernel Size Recommendations

Kernel sizes can impact smoothing differently. Typically, smaller kernel sizes (around 2-5) are best for minor smoothing, while larger sizes (around 7-9) may be used for heavily noisy images.

Well-defined Edges

If maintaining edge definition is crucial, make sure to experiment with lower sigma values while balancing kernel size and noise reduction.

Using Other Filters

You can combine this block with Blur or Denoising blocks for multi-step noise reduction workflows.

🛠️ Troubleshooting

Blurry Output

If your output appears overly blurred, try reducing the kernel size or sigma value. A smaller kernel might help retain more detail.

Input Image Not Showing

Ensure that you are correctly linking a supported image type to the block's input. If the input image is invalid, the block won't execute successfully.