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Super Resolution Node
The
Super Resolution node in AugeLab Studio is used to improve the quality of an image and upscale it using super-resolution techniques. It applies deep learning models to enhance the details and sharpness of the input image.The
Super Resolution node takes an input image and applies a selected super-resolution model to enhance its quality. It uses pre-trained models trained on different scales and quality levels to provide a range of options for super-resolution.- 1.Drag and drop the
Super Resolutionnode from the node library onto the canvas in AugeLab Studio. - 2.Connect the input image to the node's input socket.
- 3.Select the desired super-resolution model from the dropdown menu in the node's properties panel.
- 4.Run the pipeline or execute the node to process the input image and generate the super-resolved output.
- 5.Retrieve the super-resolved image from the node's output socket.
The
Super Resolution node uses the OpenCV cv2.dnn_superres module to perform super-resolution on the input image. It follows these steps during its implementation:- 1.Initialization:
- The node initializes the super-resolution model using the selected configuration.
- It loads the pre-trained model file and sets the backend and target preferences.
- 2.User Interface:
- The node provides a dropdown menu to select the desired super-resolution model.
- The available options include different models with varying trade-offs between speed and quality.
- 3.Super Resolution:
- The node receives an input image and applies the selected super-resolution model to enhance its quality.
- It uses the
cv2.dnn_superresmodule to perform the upscaling and enhancement.
- 4.Output:
- The node outputs the super-resolved image as the result of the super-resolution process.
- 1.Drag and drop the
Super Resolutionnode from the node library onto the canvas in AugeLab Studio. - 2.Connect the input image to the node's input socket.
- 3.Select the desired super-resolution model from the dropdown menu in the node's properties panel.
- 4.Run the pipeline or execute the node to process the input image and generate the super-resolved output.
- 5.Retrieve the super-resolved image from the node's output socket.
- The
Super Resolutionnode enhances the quality of an input image using deep learning-based super-resolution techniques. - It provides a range of pre-trained models with different trade-offs between speed and quality.
- The node uses the OpenCV
cv2.dnn_superresmodule to perform super-resolution. - Users can select the desired super-resolution model from a dropdown menu.
- The node outputs the super-resolved image as the result of the super-resolution process.
- The
Super Resolutionnode is useful for tasks that require improving image quality and enhancing details, such as image restoration, upscaling, and image enhancement.