Denoising of an image refers to the process of reconstruction of a signal from noisy images. Denoising is done to remove unwanted noise from image to analyze it in better form. It refers to one of the major pre-processing steps. There are four functions in opencv which is used for denoising of different images.
Syntax: cv2.fastNlMeansDenoisingColored( P1, P2, float P3, float P4, int P5, int P6)
P1 – Source Image Array
P2 – Destination Image Array
P3 – Size in pixels of the template patch that is used to compute weights.
P4 – Size in pixels of the window that is used to compute a weighted average for the given pixel.
P5 – Parameter regulating filter strength for luminance component.
P6 – Same as above but for color components // Not used in a grayscale image.
Below is the implementation:
- Python | Grayscaling of Images using OpenCV
- Measure similarity between images using Python-OpenCV
- Python - Process images of a video using OpenCV
- Erosion and Dilation of images using OpenCV in python
- Addition and Blending of images using OpenCV in Python
- Python OpenCV - Depth map from Stereo Images
- Drawing with Mouse on Images using Python-OpenCV
- Python | Create video using multiple images using OpenCV
- Arithmetic Operations on Images using OpenCV | Set-2 (Bitwise Operations on Binary Images)
- OpenCV C++ Program to create a single colored blank image
- Stitching input images (panorama) using OpenCV with C++
- Draw geometric shapes on images using OpenCV
- Arithmetic Operations on Images using OpenCV | Set-1 (Addition and Subtraction)
- Draw Colored Solid Cube using Turtle in Python
- OpenCV - Facial Landmarks and Face Detection using dlib and OpenCV
- Reading images in Python
- Working with Images in Python
- Extract images from video in Python
- Working with Images in Python using Matplotlib
- Python | Display images with PyGame
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