Denoising colored images remove unwanted noise or artifacts from an image to improve its visual quality or make it more suitable for further analysis or processing. In computer vision and image processing, noise can arise from various sources, such as image sensors, transmission or storage errors, or digital processing algorithms.
OpenCV is a popular open-source computer vision library with many functions and methods to denoise colored images in Python. Some of the most commonly used techniques for denoising include:
- Bilateral filtering: a non-linear filtering method that preserves edges while smoothing noise by applying a weighted average of nearby pixels.
- Non-local means denoising: a method that replaces the value of each pixel with a weighted average of similar pixels in the image based on their distance and similarity.
- Median filtering: a simple but effective method that replaces every pixel with the median value of its neighboring pixels.
OpenCV provides implementations of these methods in Python, which can be used to denoise colored images. To apply these methods, one needs first to read the image in OpenCV, then apply the denoising method of choice with appropriate parameters, and finally, display or save the denoised image.
Denoising colored images using OpenCV in Python can help improve the quality and clarity of images, making them more suitable for various computer vision applications, such as object recognition, segmentation, or tracking. It can also help reduce the effects of noise on subsequent image analysis or processing, leading to more accurate and reliable results.
Here is an example of how to Denoise the colored image using an open cv in Python:
Preview of the output that you will get on running this code from your IDE
In this solution we use the numPy and openCV library.
import numpy as np import matplotlib.pyplot as plt import cv2 # Read Image img = cv2.imread('input.jpg') # BGR --> RGB RGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # BGR --> Gray gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # Set thresholds th_white = 210 th_black = 85 # copy original gray mask_white = gray.copy() mask_black = gray.copy() # Thresholding mask_white[mask_white<th_white] = 0 mask_black[mask_black<th_black] = 0 mask_white[mask_white>=th_white] = 255 mask_black[mask_black>=th_black] = 255 # Median Filtering (you can remove if the text is not readable) median_white = cv2.medianBlur(mask_white,5) median_black = cv2.medianBlur(mask_black,5) # Mask 3 channels mask_white_3 = np.stack([median_white, median_white, median_white], axis=2) mask_black_3 = np.stack([median_black, median_black, median_black], axis=2) # Masking the image(in RGB) result1 = np.maximum(mask_white_3, RGB) result2 = np.minimum(mask_black_3, result1) # Visualize the results plt.imshow(result2) plt.axis('off') plt.show()
- Copy the code using the "Copy" button above, and paste it in a Python file in your IDE.
- Modify the name, location of the image to be read in the code.
- Run the file to get the Output
I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.
I found this code snippet by searching for "How to denoise the image" in kandi. You can try any such use case!
Add your Image path in line 14 .
I tested this solution in the following versions. Be mindful of changes when working with other versions.
- The solution is created in Python 3.7.15. Version
- The solution is tested on numPy 1.21.6 Version
- The solution is tested on OpenCV 4.6.0 Version
Using this solution, we can able to Denoise the image using python with the help of OpenCV library. This process also facilities an easy to use, hassle free method to create a hands-on working version of code which would help us Denoise the image in python.