
- Scikit Image – Introduction
- Scikit Image - Image Processing
- Scikit Image - Numpy Images
- Scikit Image - Image datatypes
- Scikit Image - Using Plugins
- Scikit Image - Image Handlings
- Scikit Image - Reading Images
- Scikit Image - Writing Images
- Scikit Image - Displaying Images
- Scikit Image - Image Collections
- Scikit Image - Image Stack
- Scikit Image - Multi Image
- Scikit Image - Data Visualization
- Scikit Image - Using Matplotlib
- Scikit Image - Using Ploty
- Scikit Image - Using Mayavi
- Scikit Image - Using Napari
- Scikit Image - Color Manipulation
- Scikit Image - Alpha Channel
- Scikit Image - Conversion b/w Color & Gray Values
- Scikit Image - Conversion b/w RGB & HSV
- Scikit Image - Conversion to CIE-LAB Color Space
- Scikit Image - Conversion from CIE-LAB Color Space
- Scikit Image - Conversion to luv Color Space
- Scikit Image - Conversion from luv Color Space
- Scikit Image - Image Inversion
- Scikit Image - Painting Images with Labels
- Scikit Image - Contrast & Exposure
- Scikit Image - Contrast
- Scikit Image - Contrast enhancement
- Scikit Image - Exposure
- Scikit Image - Histogram Matching
- Scikit Image - Histogram Equalization
- Scikit Image - Local Histogram Equalization
- Scikit Image - Tinting gray-scale images
- Scikit Image - Image Transformation
- Scikit Image - Scaling an image
- Scikit Image - Rotating an Image
- Scikit Image - Warping an Image
- Scikit Image - Affine Transform
- Scikit Image - Piecewise Affine Transform
- Scikit Image - ProjectiveTransform
- Scikit Image - EuclideanTransform
- Scikit Image - Radon Transform
- Scikit Image - Line Hough Transform
- Scikit Image - Probabilistic Hough Transform
- Scikit Image - Circular Hough Transforms
- Scikit Image - Elliptical Hough Transforms
- Scikit Image - Polynomial Transform
- Scikit Image - Image Pyramids
- Scikit Image - Pyramid Gaussian Transform
- Scikit Image - Pyramid Laplacian Transform
- Scikit Image - Swirl Transform
- Scikit Image - Morphological Operations
- Scikit Image - Erosion
- Scikit Image - Dilation
- Scikit Image - Black & White Tophat Morphologies
- Scikit Image - Convex Hull
- Scikit Image - Generating footprints
- Scikit Image - Isotopic Dilation & Erosion
- Scikit Image - Isotopic Closing & Opening of an Image
- Scikit Image - Skelitonizing an Image
- Scikit Image - Morphological Thinning
- Scikit Image - Masking an image
- Scikit Image - Area Closing & Opening of an Image
- Scikit Image - Diameter Closing & Opening of an Image
- Scikit Image - Morphological reconstruction of an Image
- Scikit Image - Finding local Maxima
- Scikit Image - Finding local Minima
- Scikit Image - Removing Small Holes from an Image
- Scikit Image - Removing Small Objects from an Image
- Scikit Image - Filters
- Scikit Image - Image Filters
- Scikit Image - Median Filter
- Scikit Image - Mean Filters
- Scikit Image - Morphological gray-level Filters
- Scikit Image - Gabor Filter
- Scikit Image - Gaussian Filter
- Scikit Image - Butterworth Filter
- Scikit Image - Frangi Filter
- Scikit Image - Hessian Filter
- Scikit Image - Meijering Neuriteness Filter
- Scikit Image - Sato Filter
- Scikit Image - Sobel Filter
- Scikit Image - Farid Filter
- Scikit Image - Scharr Filter
- Scikit Image - Unsharp Mask Filter
- Scikit Image - Roberts Cross Operator
- Scikit Image - Lapalace Operator
- Scikit Image - Window Functions With Images
- Scikit Image - Thresholding
- Scikit Image - Applying Threshold
- Scikit Image - Otsu Thresholding
- Scikit Image - Local thresholding
- Scikit Image - Hysteresis Thresholding
- Scikit Image - Li thresholding
- Scikit Image - Multi-Otsu Thresholding
- Scikit Image - Niblack and Sauvola Thresholding
- Scikit Image - Restoring Images
- Scikit Image - Rolling-ball Algorithm
- Scikit Image - Denoising an Image
- Scikit Image - Wavelet Denoising
- Scikit Image - Non-local means denoising for preserving textures
- Scikit Image - Calibrating Denoisers Using J-Invariance
- Scikit Image - Total Variation Denoising
- Scikit Image - Shift-invariant wavelet denoising
- Scikit Image - Image Deconvolution
- Scikit Image - Richardson-Lucy Deconvolution
- Scikit Image - Recover the original from a wrapped phase image
- Scikit Image - Image Inpainting
- Scikit Image - Registering Images
- Scikit Image - Image Registration
- Scikit Image - Masked Normalized Cross-Correlation
- Scikit Image - Registration using optical flow
- Scikit Image - Assemble images with simple image stitching
- Scikit Image - Registration using Polar and Log-Polar
- Scikit Image - Feature Detection
- Scikit Image - Dense DAISY Feature Description
- Scikit Image - Histogram of Oriented Gradients
- Scikit Image - Template Matching
- Scikit Image - CENSURE Feature Detector
- Scikit Image - BRIEF Binary Descriptor
- Scikit Image - SIFT Feature Detector and Descriptor Extractor
- Scikit Image - GLCM Texture Features
- Scikit Image - Shape Index
- Scikit Image - Sliding Window Histogram
- Scikit Image - Finding Contour
- Scikit Image - Texture Classification Using Local Binary Pattern
- Scikit Image - Texture Classification Using Multi-Block Local Binary Pattern
- Scikit Image - Active Contour Model
- Scikit Image - Canny Edge Detection
- Scikit Image - Marching Cubes
- Scikit Image - Foerstner Corner Detection
- Scikit Image - Harris Corner Detection
- Scikit Image - Extracting FAST Corners
- Scikit Image - Shi-Tomasi Corner Detection
- Scikit Image - Haar Like Feature Detection
- Scikit Image - Haar Feature detection of coordinates
- Scikit Image - Hessian matrix
- Scikit Image - ORB feature Detection
- Scikit Image - Additional Concepts
- Scikit Image - Render text onto an image
- Scikit Image - Face detection using a cascade classifier
- Scikit Image - Face classification using Haar-like feature descriptor
- Scikit Image - Visual image comparison
- Scikit Image - Exploring Region Properties With Pandas
Scikit Image - Pyramid Laplacian Transform
The Laplacian Pyramid Transform is often referred to as the Laplacian pyramid, and it is a multi-scale representation of an image used for various image processing tasks such as image compression and image enhancement. It is derived from the Gaussian pyramid and is designed to emphasize and extract details or high-frequency components from an image.
In scikit-image, you can use the skimage.transform.pyramid_laplacian() function to create a Laplacian Pyramid from an input image.
Using the skimage.transform.pyramid_laplacian() function
The skimage.transform.pyramid_laplacian() function is used to generate a sequence of images in the Laplacian pyramid created from the input image.
In each layer, the image contains the difference between the downsampled and the downsampled, smoothed image according to the formula:
layer = resize(prev_layer) - smooth(resize(prev_layer))
The initial layer of the pyramid represents the difference between the original, unscaled image and its smoothed version. The total count of images in the pyramid is determined by max_layer + 1. When all layers are computed, the last image could either be a one-pixel image or an image where further reduction does not alter its dimensions.
Syntax
Following is the syntax of this function −
skimage.transform.pyramid_laplacian(image, max_layer=-1, downscale=2, sigma=None, order=1, mode='reflect', cval=0, preserve_range=False, *, channel_axis=None)
Parameters
- image (ndarray): Input image.
- max_layer (int, optional): Number of layers for the pyramid. The 0th layer corresponds to the original image. Default is -1, which generates all possible layers.
- sigma (float, optional): Sigma value for Gaussian filter. Default is calculated as 2 * downscale / 6.0, which covers more than 99% of the Gaussian distribution.
- mode (str, optional): Determines how the array borders are handled, with cval as the value when mode is 'constant'. It can take the following strings {reflect, constant, edge, symmetric, wrap}.
- cval (float, optional): Value to fill beyond the edges of the input image if the mode is 'constant'.
- preserve_range (bool, optional): Whether to maintain the original value range. If False, the input image is converted according to img_as_float conventions.
- channel_axis (int or None, optional): Indicates the array's axis corresponding to channels. If None, the image is assumed to be grayscale (single channel). This parameter was added in version 0.19.
Return Value
It returns a generator(pyramid ) that yields pyramid layers as float images.
Example
Here is an example of building an image pyramid using the pyramid_laplacian() function.
import numpy as np import matplotlib.pyplot as plt from skimage.transform import pyramid_laplacian from skimage import io # Load the input image image = io.imread('Images/Yellow Car.jpg') # Build the Laplacian pyramid pyramid = tuple(pyramid_laplacian(image, max_layer=1, downscale=10, channel_axis=-1)) # Display the original and each layer of the Laplacian pyramid num_layers = len(pyramid) # Create subplots fig, axes = plt.subplots(1, 3, figsize=(12, 4)) # Display the original image axes[0].imshow(image) axes[0].set_title("Original Image") axes[0].axis('off') # Display each layer of the Laplacian pyramid for i, layer in enumerate(pyramid): axes[i + 1].imshow(layer) axes[i + 1].set_title(f'Layer {i + 1}') axes[i + 1].axis('off') plt.tight_layout() plt.show()
Output
On executing the above program, you will get the following output −
