488new1 - Powered by Google Docs. Non-local Means Denoising. Antoni Buades, Bartomeu Coll, Jean-Michel Morel → BibTeX published reference Antoni Buades, Bartomeu Coll, and Jean-Michel Morel, Non-Local Means Denoising, Image Processing On Line, 1 (2011). Communicated by Guoshen Yu Demo edited by Miguel Colom Antoni Buades toni.buades@uib.es, CNRS-Paris DescartesBartomeu Coll tomeu.coll@uib.es, Universitat Illes BalearsJean-Michel Morelmorel@cmla.ens-cachan.fr, CMLA, ENS-Cachan In any digital image, the measurement of the three observed color values at each pixel is subject to some perturbations.
The principle of the first denoising methods was quite simple: Replacing the color of a pixel with an average of the colors of nearby pixels. The most similar pixels to a given pixel have no reason to be close at all. Where d(B(p), B(q)) is an Euclidean distance between image patches centered respectively at p and q, f is a decreasing function and C(p) is the normalizing factor. A. Image credits. OSU Vision Lab. The CSIQ image database The CSIQ database is a new database released by our Lab.
It consists of 30 original images, each is distorted using six different types of distortions at four to five different levels of distortion. CSIQ images are subjectively rated base on a linear displacement of the images across four calibrated LCD monitors placed side by side with equal viewing distance to the observer. The database contains 5000 subjective ratings from 35 different observers, and ratings are reported in the form of DMOS. If you use this database, please cite the following paper: E.
This page is for the CSIQ image database, the video database can be found here. Cs766report.doc - Powered by Google Docs. Files\5LL70 Mod 03 Instr SZinger Restoration_and_colors_part2.pdf (application/pdf Object) Adaptive Median Filtering.doc. Adpmedian.m - Matlab implementation by the adaptive me - Source Codes Reader - HackChina. Adaptive Median Filtering.doc. Salt & Pepper noise reduction. Work - Noise Reduction By Image Averaging. Image noise can compromise the level of detail in your digital or film photos, and so reducing this noise can greatly enhance your final image or print. The problem is that most techniques to reduce or remove noise always end up softening the image as well.
Some softening may be acceptable for images consisting primarily of smooth water or skies, but foliage in landscapes can suffer with even conservative attempts to reduce noise. This section compares a couple common methods for noise reduction, and also introduces an alternative technique: averaging multiple exposures to reduce noise. Image averaging is common in high-end astrophotography, but is arguably underutilized for other types of low-light and night photography. Image averaging works on the assumption that the noise in your image is truly random. The above plot represents brightness fluctuations along thin blue and red strips of pixels in the top and bottom images, respectively.
Noise reduction by Averaging. What?! Averaging? You mean blurring? No. This method does not use any type of blurring to reduce noise in a particular image. Infact, the result is the opposite blurring: you get sharp images! In the averaging method, it is assumed that you have several images of the same object… each with a different “noise pattern”. This is exactly what can be easily obtained when taking pictures of distant galaxies. You take a coordinate, sum up the value at the position in all images. We’ll look at why this works on the next page. Getting ready for the experiment! Firstly, you need to get multiple pictures… each having some noise. Download a pack of 25 images with noise [to correct link] Next, create a new project.
Juggling code First, make sure you include the OpenCV library files cv.lib cvaux.lib cxcore.lib highgui.lib. Now, onto the main function. We begin by declaring three array of 25 image… one for each channel. Lets go through the above code line by line. Now for the real stuff. Very noisy right?