Preprocessing for image information processing

Preprocessing for image information processing

In image information processing, preprocessing has a significant impact on model performance and convergence speed, and is an important step in converting image data into a form suitable for the model. The following sections describe preprocessing methods for image information processing.

Resizing and Cropping

Resizing and cropping in image information processing are important preprocessing methods for converting image data into a form suitable for input to a model. Image cropping is sometimes used to change the resolution of an image to reduce computational cost and to unify the input size to the model. Each of these methods is described below.

<Resizing>

Resizing is a technique to change the resolution of an image, which is done to reduce computational cost and to provide a uniform input size to the model. The main resizing methods include the following

Resize to specified size:

Resize the image to a specified size. This allows for a uniform input size for the model.

from PIL import Image
import torchvision.transforms as transforms

# Loading Images
image = Image.open("example.jpg")

# Resize by size specification
resized_image = transforms.Resize((224, 224))(image)

Resize while maintaining aspect ratio:

Resizes the image to the specified width or height while maintaining the aspect ratio.

from PIL import Image
import torchvision.transforms as transforms

# Loading Images
image = Image.open("example.jpg")

# Resize by specifying width and maintaining aspect ratio
resized_image = transforms.Resize((224, 0))(image)

<Cropping>

Cropping is a method of extracting a specified region from an image. This allows for the extraction of regions of interest within an image. The main cropping methods are as follows

Random cropping:

Crops a region of a specified size from an image at a random location. This is used as part of data enhancement.

from PIL import Image
import torchvision.transforms as transforms

# Loading Images
image = Image.open("example.jpg")

# random cropping
random_crop = transforms.RandomCrop((100, 100))(image)

Center Crop:

Crops an area of the specified size from the center of the image.

from PIL import Image
import torchvision.transforms as transforms

# Loading Images
image = Image.open("example.jpg")

# center cropping
center_crop = transforms.CenterCrop((100, 100))(image)

These resizing and cropping techniques are frequently used in converting image data to the appropriate input format for the model, and it is important to select the appropriate resizing and cropping techniques for the specific requirements of the data set and model.

Normalization

In image information processing, normalization (Normalization) stabilizes the learning process by normalizing the pixel values of an image, usually by scaling the pixel values from 0 to 1 or transforming them so that the mean is 0 and the standard deviation is 1. Normalization aids model convergence and serves to make the learning process more efficient. The following is an example of a normalization method and its implementation.

Scaling to [0, 1]:

Scales the pixel values of an image from 0 to 1. This is generally one of the most effective normalization methods in deep learning models.

from PIL import Image
import torchvision.transforms as transforms

# Loading Images
image = Image.open("example.jpg")

# Scaling to [0, 1]
normalize = transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
normalized_image = transforms.ToTensor()(image)
normalized_image = normalize(normalized_image)

Normalize so that the mean is 0 and the standard deviation is 1:

Transforms pixel values so that the mean is 0 and the standard deviation is 1. This results in data centralization.

from PIL import Image
import torchvision.transforms as transforms

# Loading Images
image = Image.open("example.jpg")

# Normalized so that the mean is 0 and the standard deviation is 1
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
normalized_image = transforms.ToTensor()(image)
normalized_image = normalize(normalized_image)

These normalization methods have the effect of constraining the value range of the image data, making it easier for the model to learn. In particular, it is expected that the scale of pixel values across different images will be unified, thus stabilizing the learning process and effectively facilitating convergence.

Subtraction of averages

Mean subtraction refers to the process of subtracting the mean value from the pixel value of the image data, which centers the data and makes the model easier to converge and more efficient in learning. Usually, in the case of RGB images, the mean value is subtracted for each channel. The following is a method of mean value subtraction and an example of its implementation.

Mean value subtraction:

By subtracting the mean value from the pixel values, the image data is centralized. Centering makes it easier for the model to learn the characteristics of the data.

from PIL import Image
import torchvision.transforms as transforms

# Loading Images
image = Image.open("example.jpg")

# Subtraction of averages
mean_subtraction = transforms.Normalize(mean=[-0.485, -0.456, -0.406], std=[1, 1, 1])
normalized_image = transforms.ToTensor()(image)
normalized_image = mean_subtraction(normalized_image)

In the above example, the mean value is subtracted using transforms.Normalize. The mean values used here are values commonly used in ImageNet datasets, whereby the mean is subtracted for each RGB channel of the image data and the image is preprocessed into a form suitable for the model.

This technique is typically used to preprocess images to match the statistics of the dataset on which the model was trained, and different datasets may use different average values.

Data Augmentation

Data Augmentation is a method of applying random transformations to images to increase the training data. Data augmentation can include rotation, flipping, zooming in/out, brightness changes, etc., and is widely used to improve the generalization performance of models and reduce overtraining. The following describes some common methods of data augmentation.

Data Extension Techniques:

• Random Rotation: Rotate the image at a random angle.
• Random Horizontal Flip: Flip the image horizontally at random.
• Random Vertical Flip: Flip an image vertically at random.
• Random Zoom: Randomly zooms in or out an image.
• Random Brightness Transform (Random Brightness): Randomly changes the brightness of an image.
• Random Contrast Transform (Random Contrast): Randomly changes the contrast of an image.
• Random Hue Transform (Random Hue): Randomly changes the hue of an image.

Example Data Extension Implementation:

Below is an example implementation of data enhancement using PyTorch.

import torchvision.transforms as transforms
from PIL import Image

# Loading Images
image = Image.open("example.jpg")

# Definition of Data Extension
data_transform = transforms.Compose([
    transforms.RandomRotation(degrees=15),
    transforms.RandomHorizontalFlip(),
    transforms.RandomVerticalFlip(),
    transforms.RandomResizedCrop(size=(224, 224), scale=(0.8, 1.0)),
    transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.2),
])

# Applying Data Extensions
augmented_image = data_transform(image)

In the above example, random rotation, horizontal flips, vertical flips, random resizing and cropping, and random color brightness, contrast, saturation, and hue changes are performed. These transformations help diversify the training data and improve the generalization performance of the model. Data expansion methods may be adjusted depending on the task and dataset.

color space transformation

Color space conversion refers to the process of converting the color information of pixels in an image to a color space other than RGB (e.g., grayscale, HSV), which is used in image processing and computer vision tasks when color information is not needed or when certain color information is important or processing in a different color space is beneficial It is used when processing in different color spaces is beneficial. Some common color space transformations are described below.

Grayscale Transformations:

A grayscale transformation converts an image to black and white. This is useful when the image focuses on luminance information rather than color information.

from PIL import Image
import torchvision.transforms as transforms

# Loading Images
image = Image.open("example.jpg")

# grayscale conversion
grayscale_transform = transforms.Grayscale()
grayscale_image = grayscale_transform(image)

HSV Transform:

The HSV (Hue, Saturation, Value) transform is a color space that represents an image based on three factors: hue, saturation, and lightness. It is used when changes in saturation or lightness are necessary.

from PIL import Image
import colorsys

# Loading Images
image = Image.open("example.jpg")

# HSV conversion
hsv_image = image.convert("HSV")

# Obtaining Hue, Saturation, and Value
h, s, v = hsv_image.split()

LAB Transform:

The LAB transform will split color information into three components: lightness (L), a-axis (green to magenta), and b-axis (blue to yellow). It is used when color change is critical.

from PIL import Image
import cv2
import numpy as np

# Loading Images
image = Image.open("example.jpg")

# RGB to LAB conversion using OpenCV
image_np = np.array(image)
lab_image = cv2.cvtColor(image_np, cv2.COLOR_RGB2LAB)

In these examples, PIL (Python Imaging Library) and OpenCV are used for color space conversion. It is important to select the appropriate color space transformation based on the task and specific requirements.

Noise Removal

In the preprocessing of image information, noise occurs when image data is inaccurate or contains unwanted information, and noise reduction is an important step in removing unwanted noise from an image. The following is a general description of noise reduction techniques.

Smoothing Filters:

Smoothing filters remove noise by averaging each pixel in the image with its surrounding pixels. Typical smoothing filters include Gaussian and median filters.

Gaussian filter: A filter that removes noise by averaging each pixel in the image with the surrounding pixels.

from PIL import Image
from scipy.ndimage import gaussian_filter

# Loading Images
image = Image.open("example.jpg")

# Apply Gaussian filter
smoothed_image = gaussian_filter(image, sigma=1)

Median filter:

from PIL import ImageFilter

# Loading Images
image = Image.open("example.jpg")

# Apply median filter
smoothed_image = image.filter(ImageFilter.MedianFilter(size=3))

Wavelet Transform:

Wavelet transform is a method of decomposing an image into its frequency components, allowing noise to be removed in specific frequency bands.

import cv2
import numpy as np

# Loading Images
image = cv2.imread("example.jpg", cv2.IMREAD_GRAYSCALE)

# wavelet transform
coeffs = cv2.dwt2(image, 'bior1.3')
cA, (cH, cV, cD) = coeffs

# Image with noise removed
denoised_image = cv2.idwt2((cA * 0, (cH, cV, cD)), 'bior1.3')

Non-local Means Denoising:

Non-local means denoising is a method of removing noise by comparing each pixel in an image with other pixels and using the average of similar regions.

from skimage.restoration import denoise_nl_means

# Loading Images
image = cv2.imread("example.jpg", cv2.IMREAD_GRAYSCALE)

# Apply non-local mean-reduction
denoised_image = denoise_nl_means(image, h=0.8)

These methods have different effects for different types of noise and images, and the selection of the appropriate denoising method depends on the specific problem and requirements.

edge detection

Edge detection is an image processing technique for detecting the boundaries (edges) of objects and structures in an image. It emphasizes edge information to capture the shape of objects and is used as a preprocessing step for advanced tasks such as object detection and segmentation. The following describes some common methods for edge detection.

Sobel Filter:

The Sobel filter detects edges by calculating the gradients of the image in the x-axis and y-axis directions and combining them.

import cv2
import numpy as np

# Loading Images
image = cv2.imread("example.jpg", cv2.IMREAD_GRAYSCALE)

# Apply Sobel filter
sobel_x = cv2.Sobel(image, cv2.CV_64F, 1, 0, ksize=3)
sobel_y = cv2.Sobel(image, cv2.CV_64F, 0, 1, ksize=3)

# Calculate edge strength
edge_intensity = np.sqrt(sobel_x**2 + sobel_y**2)

# Normalize edge images to a range of 0 to 255
normalized_edge = cv2.normalize(edge_intensity, None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8)

Canny Edge Detection:

Canny edge detection uses a multi-step algorithm to detect edges. This includes smoothing with a Gaussian filter, computing gradients, non-maximum suppression, and hysteresis thresholding.

import cv2

# Loading Images
image = cv2.imread("example.jpg", cv2.IMREAD_GRAYSCALE)

# Apply Canny edge detection
edges = cv2.Canny(image, 50, 150)

Laplacian Filter:

The Laplacian filter detects edges by computing the second-order derivative of the image, making it a useful method for capturing edge changes.

import cv2

# Loading Images
image = cv2.imread("example.jpg", cv2.IMREAD_GRAYSCALE)

# Apply Laplacian filter
laplacian = cv2.Laplacian(image, cv2.CV_64F)

# Normalize edge images to a range of 0 to 255
normalized_laplacian = cv2.normalize(laplacian, None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8)

These methods are the basic methods for edge detection, and some specific tasks may use a combination of these methods, and the method selected will depend on the requirements of the task and the characteristics of the image data.

Pre-processing specific to a particular task

Preprocessing specific to a particular task is done to prepare the input data for the model in a form suitable for the task and to improve model training and inference. This could be, for example, cropping or resizing the regions generated by a region suggestion network (RPN) in the case of object detection. The following sections describe preprocessing methods specific to some of the tasks. 1.

1. object detection:

Object detection tasks usually require identifying the location and class of objects in the image, and common preprocessing methods include the following

• Anchor box generation: Object detection models use anchor boxes to predict the position of objects. Anchor boxes represent candidate regions in the image. For more information on anchor box, see “Overview of anchor boxes in object detection and related algorithms and implementation examples“
• Data augmentation: Data augmentation, such as random cropping, rotation, and flipping, is applied to the training data to improve the generalization performance of the model.

2. image segmentation:

Image segmentation assigns each pixel in an image to a different object class. Specific preprocessing includes

• Mask generation: For each image, a segmentation mask is generated to indicate object regions.
• Size unification: Unify the size of the input images to facilitate input to the model.

3. face detection:

Face detection is the task of detecting faces in an image and is used for face recognition and surveillance.

• Face Normalization: Faces are centered and normalized to a specific size as input to a face detection model.
• Data Enhancement: random crops and flips are applied to the training data to improve the performance of the model.

Reference Information and Reference Books

For details on image information processing, see “Image Information Processing Techniques.

Reference book is “Image Processing and Data Analysis with ERDAS IMAGINE“

“Hands-On Image Processing with Python: Expert techniques for advanced image analysis and effective interpretation of image data“

“Introduction to Image Processing Using R: Learning by Examples“

“Deep Learning for Vision Systems“