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Blur Detector
The
Blur Detector node in AugeLab Studio is used to determine whether an input image is blurry or not. It utilizes the Fourier Transform to analyze the image's frequency spectrum and compute a blur metric based on the mean magnitude of the spectrum.The
Blur Detector node calculates a blur metric for an input image using the Fourier Transform. It compares the mean magnitude of the frequency spectrum with a specified threshold to determine whether the image is blurry or not. The node outputs a Boolean value indicating the result.- Node Title: Blur Detector
- Node ID: OP_NODE_BLUR_DETECTOR
The
Blur Detector node has the following input socket:- 1.Input Image: The input image to be analyzed for blur detection.
The
Blur Detector node has the following output socket:- 1.Is Blurred?: A Boolean value indicating whether the input image is blurry (
True) or not (False).
The
Blur Detector node provides the following adjustable parameter:- Detection Threshold: Specifies the threshold value for the blur detection. The image will be considered blurry if the calculated blur metric (mean magnitude) is less than or equal to this threshold.
- 1.Drag and drop the
Blur Detectornode from the node library onto the canvas in AugeLab Studio. - 2.Connect the input image to the Input Image input socket of the
Blur Detectornode. - 3.Adjust the Detection Threshold slider to set the desired threshold for blur detection.
- 4.Run the pipeline.
- 5.The
Blur Detectornode will analyze the input image using the Fourier Transform and calculate a blur metric. - 6.The calculated blur metric will be compared with the specified threshold.
- 7.The output socket Is Blurred? will indicate whether the input image is blurry (
True) or not (False). - 8.The Detection Threshold value can be adjusted to fine-tune the blur detection sensitivity.
- 9.Use the output result for further processing, decision-making, or filtering based on the blur status of the input image.
- The
Blur Detectornode utilizes the Fourier Transform to analyze the frequency spectrum of the input image. - It applies a high-pass filter to remove low-frequency components from the spectrum, which correspond to blurry regions.
- The mean magnitude of the resulting spectrum is calculated as the blur metric for the image.
- If the calculated blur metric is less than or equal to the specified threshold, the image is considered blurry.
- The
Blur Detectornode is useful in various computer vision applications where identifying and filtering out blurry images is required. - Adjusting the Detection Threshold allows you to control the sensitivity of the blur detection.
- Lower threshold values will classify more images as blurry, while higher threshold values will be more strict in detecting blur.
- The calculated blur metric and the provided threshold can help in analyzing and categorizing the quality of images in image processing pipelines.
That concludes the documentation for the
Blur Detector node in AugeLab Studio. This node enables you to detect whether an input image is blurry or not using the Fourier Transform and a threshold-based metric. Use this node to identify and filter out blurry images in various computer vision applications and image processing pipelines.