Denoising

This function block is designed to reduce noise in images, producing a cleaner and noiseless output. It utilizes advanced image processing techniques to enhance image quality.

📥 Inputs

Image Any The input image that needs to be denoised. It can be either a grayscale or colored image.

📤 Outputs

Image Any The denoised output image, which will appear cleaner and free from unwanted noise.

🕹️ Controls

Strength A slider that sets the overall strength of denoising. Increasing this value can significantly improve denoising but may also affect performance.

Averaging A slider that controls the averaging power for noise reduction. Higher values may result in smoother images but can cause loss of detail and sharp edges.

Blend Noise (Colored) A slider to adjust the color blending for colored noise in the image. This value helps to blend remaining noise with the background, enhancing the overall visual quality.

🎨 Features

Customizable Parameters Users can adjust several parameters to fine-tune the denoising effect according to their specific needs.

Support for Colored Images This block can process both grayscale and colored images effectively.

📝 Usage Instructions

1. Connect Input Image: Link a noisy image to the input of this block.

2. Adjust Parameters: Use the sliders to set desired values for Strength, Averaging, and Blend Noise (Colored).

3. Evaluate: Run the block to apply the denoising process. The output will be a clearer version of the input image.

📊 Evaluation

When executed, the function block processes the input image and produces a denoised version based on the configured parameters.

💡 Tips and Tricks

Finding the Right Balance

Experiment with different values for Averaging and Blend Noise (Colored) to find the best balance between noise reduction and image detail retention.

Strength Settings

Increasing the Strength parameter may improve denoising but can sometimes lead to loss of finer details. It may be beneficial to find a middle ground for optimal results.

Preprocessing Before Denoising

For optimal results, consider preprocessing the input image for noise reduction with other methods such as Image Threshold or Blur function blocks before applying the denoising process.

🛠️ Troubleshooting

Image Does Not Change

If the output image appears unchanged, ensure that you have connected a valid noisy image to the input and adjusted the sliders appropriately.

Poor Detail Retention

If denoising results in loss of detail, try reducing the values of Averaging and Strength. Adjust these parameters gradually until you achieve the desired image quality.