ImageEnhancingUsingPython

Image Enhancement Using Python

Image enhancement is a process of improving the visual quality of an image, making it more visually appealing, easier to interpret, or better suited for a specific application. In Python, you can perform image enhancement using a combination of libraries and techniques to adjust various aspects of an image. Here is a general description of image enhancement using Python:

1. Image Loading and Preprocessing:

   The first step in image enhancement is to load the image into Python. Libraries such as Pillow or OpenCV can be used for this purpose. Once the image is loaded, you can perform preprocessing tasks such as resizing, cropping, or converting to grayscale.

2. Brightness and Contrast Adjustment:

   Adjusting the brightness and contrast of an image can significantly enhance its appearance. Python libraries like Pillow allow you to increase or decrease these parameters to improve the overall visibility of the image.

3. Histogram Equalization:

   Histogram equalization is a technique used to improve the contrast of an image by redistributing the intensity values. Python offers functions for histogram equalization, especially with libraries like OpenCV.

4. Color Correction:

   Color correction can be vital for images, especially when dealing with photographs. You can use techniques to adjust color balance, saturation, and color temperature to make the image look more natural or bring out specific colors.

5. Sharpening and Blurring:

   Image sharpness can be enhanced by using sharpening filters or techniques. On the other hand, blurring can be used to reduce noise or enhance certain stylistic effects. Libraries like Pillow and OpenCV provide functions for applying sharpening and blurring filters.

6. Noise Reduction:

   Removing noise from an image is crucial for improving image quality. Python offers various methods to reduce noise, including median filtering and Gaussian filtering.

7. Resolution Enhancement:

   Upscaling the resolution of an image, such as converting it to a higher resolution (e.g., 8K), can be done using resampling techniques provided by libraries like Pillow.

8. Image Enhancement with Machine Learning:

   Advanced image enhancement can be achieved using machine learning models. Deep learning models, such as convolutional neural networks (CNNs), can be used to enhance image quality by learning features from a dataset and applying them to the input image.

9. Post-processing and Visualization:

   After applying enhancement techniques, it's essential to visualize the results and fine-tune the settings to achieve the desired outcome. You can save the enhanced image or display it using Python's visualization libraries.

10. Automation and Batch Processing:

   Python allows for automating the image enhancement process for multiple images in a batch. This is especially useful when working with large datasets.

Image enhancement in Python is a versatile and powerful process, enabling you to improve the visual quality of images for various applications, including photography, computer vision, medical imaging, and more. The choice of enhancement techniques and libraries depends on the specific requirements of the task at hand, making Python a valuable tool for working with digital images.

GitHub Link:

You can download or clone projects:https://github.com/Sivatech24/ImageEnhancingUsingPython.git

Input Image:

The image used was downloaded form pexels website

Photo details

Uploaded on August 26th, 2016

Dimensions:2200 x 1414

Aspect Ratio:1100:707

File Size:1.27 MB

DPIAdjustment code:

from PIL import Image

# Open the image

image = Image.open('input.jpg')

# Set the desired DPI (e.g., 300 DPI)

dpi_value = (300, 300)

# Set the DPI in the image info

image.info['dpi'] = dpi_value

# Save the image with the new DPI

image.save('dpi_adjusted_image.jpg')

Output:

Image Resolution Changer code:

from PIL import Image

# Open the input image

input_image = Image.open('input.jpg')

# Define the desired width and height

width = input('Enter the width')  # Replace with your desired width

desired_width = int(width) 

height = input('Enter the height')  # Replace with your desired height

desired_height = int(height)

# Upscale the image to the desired resolution

upscaled_image = input_image.resize((desired_width, desired_height), Image.NEAREST)

# Save the upscaled image

upscaled_image.save('upscaled_image.jpg')

# Close the input image

input_image.close()

Output:

Sharpening code:

from PIL import Image, ImageFilter

# Open the input image

input_image = Image.open('input.jpg')

# Apply sharpening filter

sharpened_image = input_image.filter(ImageFilter.SHARPEN)

# Save the sharpened image

sharpened_image.save('sharpened_image.jpg')

# Close the input image

input_image.close()

Output:

Using Deep Learning for Image Upscaling code:

import cv2

import torch

import numpy as np

from esrgan_pytorch import ESRGAN

# Load the ESRGAN model

model = ESRGAN()

# Load the input image

image = cv2.imread('input_image.jpg')

# Convert BGR to RGB

image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Upscale the image using ESRGAN

upscaled_image = model.upscale(image_rgb)

# Convert the output image to BGR

upscaled_image_bgr = cv2.cvtColor(upscaled_image, cv2.COLOR_RGB2BGR)

# Save the upscaled image

cv2.imwrite('output_image.jpg', upscaled_image_bgr)

Using OpenCV for Basic Image Enhancement and Upscaling code:

import cv2

# Load the image

image = cv2.imread('input.jpg')

# Perform image enhancement (e.g., adjust brightness and contrast)

enhanced_image = cv2.convertScaleAbs(image, alpha=2.0, beta=50)

# Upscale the image

upscaled_image = cv2.resize(enhanced_image, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC)

# Save the enhanced and upscaled image

cv2.imwrite('output_image.jpg', upscaled_image)

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