# Image noise detection python

• Image Processing in Python: Algorithms, Tools, and Methods You Should Know
• Blur detection with OpenCV
• Noise in photographic images
• OpenCV and Python: Simple Noise-tolerant Motion Detector
• Denoising Images in Python – A Step-By-Step Guide
• Add a “salt and pepper” noise to an image with Python
• ## Image Processing in Python: Algorithms, Tools, and Methods You Should Know

Click here to download the source code to this post Between myself and my father, Jemma, the super-sweet, hyper-active, extra-loving family beagle may be the most photographed dog of all time. But I love dogs. A lot. Especially beagles. Over this past weekend I sat down and tried to organize the massive amount of photos in iPhoto. Not only was it a huge undertaking, I started to notice a pattern fairly quickly — there were lots of photos with excessive amounts of blurring.

Whether due to sub-par photography skills, trying to keep up with super-active Jemma as she ran around the room, or her spazzing out right as I was about to take the perfect shot, many photos contained a decent amount of blurring. Instead, I opened up an editor and coded up a quick Python script to perform blur detection with OpenCV.

Looking for the source code to this post? Variance of the Laplacian Figure 1: Convolving the input image with the Laplacian operator. My first stop when figuring out how to detect the amount of blur in an image was to read through the excellent survey work, Analysis of focus measure operators for shape-from-focus [ Pertuz et al].

Inside their paper, Pertuz et al. If you have any background in signal processing, the first method to consider would be computing the Fast Fourier Transform of the image and then examining the distribution of low and high frequencies — if there are a low amount of high frequencies, then the image can be considered blurry.

However, defining what is a low number of high frequencies and what is a high number of high frequencies can be quite problematic, often leading to sub-par results. Pertuz et al. After a quick scan of the paper, I came to the implementation that I was looking for: variation of the Laplacian by Pech-Pacheco et al.

The method is simple. Has sound reasoning. And can be implemented in only a single line of code: cv2. Laplacian image, cv2. And then take the variance i. If the variance falls below a pre-defined threshold, then the image is considered blurry; otherwise, the image is not blurry.

The reason this method works is due to the definition of the Laplacian operator itself, which is used to measure the 2nd derivative of an image. The Laplacian highlights regions of an image containing rapid intensity changes, much like the Sobel and Scharr operators. And, just like these operators, the Laplacian is often used for edge detection. The assumption here is that if an image contains high variance then there is a wide spread of responses, both edge-like and non-edge like, representative of a normal, in-focus image.

But if there is very low variance, then there is a tiny spread of responses, indicating there are very little edges in the image. As we know, the more an image is blurred, the less edges there are. Obviously the trick here is setting the correct threshold which can be quite domain dependent. Too high of a threshold then images that are actually blurry will not be marked as blurry. This method tends to work best in environments where you can compute an acceptable focus measure range and then detect outliers.

Some are blurry, some are not. Our goal is to perform blur detection with OpenCV and mark the images as such. As you can see, some images are blurry, some images are not. Our goal here is to correctly mark each image as blurry or non-blurry. ArgumentParser ap. This method will take only a single argument the image presumed to be a single channel, such as a grayscale image that we want to compute the focus measure for.

From there, Line 9 simply convolves the image with the 3 x 3 Laplacian operator and returns the variance. Lines handle parsing our command line arguments. Believe it or not, the hard part is done! We just need to write a bit of code to load the image from disk, compute the variance of the Laplacian, and then mark the image as blurry or non-blurry: loop over the input images for imagePath in paths.

Finally, Lines write the text and computed focus measure to the image and display the result to our screen. The focus measure of this image is Figure 5: Performing blur detection with OpenCV. This image has a focus measure of Figure 6 has a very high focus measure score at This image is clearly non-blurry and in-focus.

The only amount of blur in this image comes from Jemma wagging her tail. Figure 9: Computing the focus measure of an image. However, we can clearly see the above image is blurred. Figure An example of computing the amount of blur in an image. The large focus measure score indicates that the image is non-blurry. However, this image contains dramatic amounts of blur. Figure Detecting the amount of blur in an image using the variance of Laplacian.

Figure Compared to Figure 12 above, the amount of blur in this image is substantially reduced. What's next? I recommend PyImageSearch University. Do you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated? Or has to involve complex mathematics and equations?

Or requires a degree in computer science? All you need to master computer vision and deep learning is for someone to explain things to you in simple, intuitive terms. My mission is to change education and how complex Artificial Intelligence topics are taught. If you're serious about learning computer vision, your next stop should be PyImageSearch University, the most comprehensive computer vision, deep learning, and OpenCV course online today. Join me in computer vision mastery.

This method is fast, simple, and easy to apply — we simply convolve our input image with the Laplacian operator and compute the variance. Be sure to download the code using the form at the bottom of this post and give it a try! Inside you'll find my hand-picked tutorials, books, courses, and libraries to help you master CV and DL!

Download the code! All too often I see developers, students, and researchers wasting their time, studying the wrong things, and generally struggling to get started with Computer Vision, Deep Learning, and OpenCV. I created this website to show you what I believe is the best possible way to get your start. Reader Interactions.

## Blur detection with OpenCV

The middle plot shows f-stop scene-referenced noise the inverse of scene-referenced SNR. The lower plot shows the normalized pixel noise. It increases in the dark regions due to a combination of gamma-encoding and the high ISO speed.

Digital cameras achieve high ISO speed by amplifying the sensor output, which boosts noise, particularly in dark regions. This curve looks different for the minimum ISO speed: noise values are much lower and subject to more statistical variation.

SNR improves by about 6 dB for each doubling of exposure 0. In this range of illumination, shot noise is not prominent.

This curve would be dramatically different at lower ISO speeds, where shot noise has an effect. Noise summary There are two basic types of noise. Noise which varies randomly each time an image is captured.

Spatial or fixed pattern. Noise caused by sensor nonuniformities. Sensor designers have greatly reduced fixed pattern noise in the last decade. Temporal noise can be reduced by signal averaging, which involves summing N images, then dividing by N. This is an option for all Imatest analysis modules when several image files are selected you can also analyze the individual files separately.

Summing N individual images increases the summed signal pixel level voltage by N. But since temporal noise is uncorrelated, noise power rather than voltage or pixel level is summed. Several factors affect noise. Pixel size.

Simply put, the larger the pixel, the more photons reach it, and hence the better the signal-to-noise ratio SNR for a given exposure. The number of electrons generated by the photons and the full-well electron capacity is proportional to the sensor area as well as the quantum efficiency. Sensor technology and manufacturing. We observed an improvement when we compared cameras in Shannon Information Capacity. An older technology issue was CMOS vs. Until CMOS was regarded as having worse noise, but it has improved to the point where it almost completely dominates the industry.

Other aspects of sensor design and manufacturing are gradually improving with time. ISO speed Exposure Index setting. Digital cameras control ISO speed by amplifying the signal along with the noise at the pixel output. To fully characterize a sensor it should be tested at the lowest available ISO speed. For practical applications, performance at high ISO speeds is also of interest. Exposure time. Long exposures with dim light tend to be noisier than short exposures with bright light, i.

To fully characterize a sensor it should be tested at long exposure times several seconds, at least. Digital processing. When an image is converted to an 8-bit bit color JPEG, noise increases slightly. Hence it is often best to convert to bit bit color files. Output file bit depth makes little difference in the measured noise of unmanipulated files. Raw conversion. In-camera raw converters, used to create camera JPEG files, usually apply noise reduction lowpass filtering in smooth areas and sharpening near edges whether you want it or not; even if NR and sharpening are turned off.

Raw converters built into Imatest LibRaw for commercial files, Read Raw for binary files, or the Matlab demosaic function for special cases Rawview minimally process images. Output from these converters is OK for noise measurements. General comments Imatest subtracts gradual pixel level variations from the image before calculating noise the standard deviation of pixel levels in the region under test. This removes errors that could be caused by uneven lighting.

Nevertheless, you should take care to illuminate the target as evenly as possible. The target used for noise measurements should be smooth and uniform— grain in film targets or surface roughness in reflective targets should not be mistaken for sensor noise. Appropriate lighting using more than one lamp can minimize the effects of surface roughness. In any of the modules, read two images. The window shown on the right appears. Select the Read two files for measuring temporal noise radio button.

The two files will be read and their difference which cancels fixed pattern noise is taken. Since these images are independent, noise powers add. SNR dB for Colorchecker chart: temporal noise shown as thin dotted lines. From ISO , sections 6. Currently we are using simple noise not yet scene-referred noise. Select between 4 and 16 files. In the multi-image file list window shown above select Read n files for temporal noise.

Since N is a relatively small number between 4 and 16, with 8 recommended , it must be corrected using formulas from Identities and mathematical properties in the Wikipedia standard deviation page. There is a detailed comparison of the methods in Measuring Temporal Noise. Measuring noise in raw versus demosaiced images A customer recently questioned whether is was appropriate to measure noise in demosaiced images, where the signal in each color channel R, G, B is influenced by data in each of the raw color channels R, Gr, B, and Gb.

The quick answer is yes. We recommend measuring noise and SNR in the image raw or demosaiced that most closely resembles the use case. Raw images are best when measuring sensor performance, but demosaiced images are fine in most cases. This excellent question led us into a deep dive, which you can see by clicking the button below. Show the analysis of noise in raw vs. Because the differences between the raw and demosaiced channels, we cannot expect identical noise measurements.

This was done to ensure that all files were processed identically and minimally— no noise reduction, sharpening, gamma encoding or color correction. We have long been aware that demosaicing affects the frequency spectrum of the patches. The large response at low frequencies is apparently caused by small amounts of illumination nonuniformity. The frequency spectrum in the important region between 0.

But for the demosaiced image, it drops by about 0. For this reason the decrease in spectral response from 0.

## Noise in photographic images

Laplacian image, cv2. And then take the variance i. If the variance falls below a pre-defined threshold, then the image is considered blurry; otherwise, the image is not blurry.

The reason this method works is due to the definition of the Laplacian operator itself, which is used to measure the 2nd derivative of an image.

## OpenCV and Python: Simple Noise-tolerant Motion Detector

The Laplacian highlights regions of an image containing rapid intensity changes, much like the Sobel and Scharr operators. And, just like these operators, the Laplacian is often used for edge detection. The assumption here is that if an image contains high variance then there is a wide spread of responses, both edge-like and non-edge like, representative of a normal, in-focus image.

But if there is very low variance, then there is a tiny spread of responses, indicating there are very little edges in the image. As we know, the more an image is blurred, the less edges there are. Obviously the trick here is setting the correct threshold which can be quite domain dependent.

Too high of a threshold then images that are actually blurry will not be marked as blurry. This method tends to work best in environments where you can compute an acceptable focus measure range and then detect outliers. Some are blurry, some are not. Our goal is to perform blur detection with OpenCV and mark the images as such.

As you can see, some images are blurry, some images are not. Our goal here is to correctly mark each image as blurry or non-blurry. ArgumentParser ap. This method will take only a single argument the image presumed to be a single channel, such as a grayscale image that we want to compute the focus measure for.

From there, Line 9 simply convolves the image with the 3 x 3 Laplacian operator and returns the variance. Lines handle parsing our command line arguments. Believe it or not, the hard part is done! We just need to write a bit of code to load the image from disk, compute the variance of the Laplacian, and then mark the image as blurry or non-blurry: loop over the input images for imagePath in paths.

Finally, Lines write the text and computed focus measure to the image and display the result to our screen. Spatial or fixed pattern.

### Denoising Images in Python – A Step-By-Step Guide

Noise caused by sensor nonuniformities. Sensor designers have greatly reduced fixed pattern noise in the last decade. Temporal noise can be reduced by signal averaging, which involves summing N images, then dividing by N. This is an option for all Imatest analysis modules when several image files are selected you can also analyze the individual files separately.

Summing N individual images increases the summed signal pixel level voltage by N. But since temporal noise is uncorrelated, noise power rather than voltage or pixel level is summed. Several factors affect noise. Pixel size. Simply put, the larger the pixel, the more photons reach it, and hence the better the signal-to-noise ratio SNR for a given exposure.

The number of electrons generated by the photons and the full-well electron capacity is proportional to the sensor area as well as the quantum efficiency. Sensor technology and manufacturing. We observed an improvement when we compared cameras in Shannon Information Capacity.

An older technology issue was CMOS vs. Until CMOS was regarded as having worse noise, but it has improved to the point where it almost completely dominates the industry. Other aspects of sensor design and manufacturing are gradually improving with time. ISO speed Exposure Index setting. Digital cameras control ISO speed by amplifying the signal along with the noise at the pixel output. To fully characterize a sensor it should be tested at the lowest available ISO speed.

For practical applications, performance at high ISO speeds is also of interest. Exposure time. Long exposures with dim light tend to be noisier than short exposures with bright light, i. To fully characterize a sensor it should be tested at long exposure times several seconds, at least.

The kernel makes horizontal and vertical shifts based on the stride rate until the full image is traversed. It helps to decrease the computational power required to process the data. Max pooling returns the maximum value from the area covered by the kernel on the image.

Average pooling returns the average of all the values in the part of the image covered by the kernel. Fully connected layers Source CNN is mainly used in extracting features from the image with help of its layers. CNNs are widely used in image classification where each input image is passed through the series of layers to get a probabilistic value between 0 and 1.

Generative Adversarial Networks Generative models use an unsupervised learning approach there are images but there are no labels provided. GANs are composed of two models Generator and Discriminator. Generator learns to make fake images that look realistic so as to fool the discriminator and Discriminator learns to distinguish fake from real images it tries not to get fooled.

Generator is not allowed to see the real images, so it may produce poor results in the starting phase while the discriminator is allowed to look at real images but they are jumbled with the fake ones produced by the generator which it has to classify as real or fake. Based on the scores predicted by the discriminator, the generator tries to improve its results, after a certain point of time, the generator will be able to produce images that will be harder to distinguish, at that point of time, the user gets satisfied with its results.

Discriminator also improves itself as it gets more and more realistic images at each round from the generator. GANs are great for image generation and manipulation.

Image processing tools 1. There are several ways you can use opencv in image processing, a few are listed below: Converting images from one color space to another i.

Performing mga taong nakaranas ng diskriminasyon on images, like, simple thresholding, adaptive thresholding etc. Smoothing of images, like, applying custom filters to images and blurring of images.

Performing morphological operations on images. Building image pyramids. Extracting foreground from images using GrabCut algorithm. Image segmentation using watershed algorithm. Refer to this link for more details. Scikit-image It is an open-source library used for image preprocessing. It makes use of machine learning with built-in functions and can perform complex operations on images with just a few functions.

### Add a “salt and pepper” noise to an image with Python

It works with numpy arrays and is a fairly simple library even for those who are new to python. It will use seven global thresholding algorithms. This is in the filters module. To implement edge detection use sobel method in the filters module.

This method requires a 2D grayscale image as an input, so we need to convert the image to grayscale. To implement gaussian smoothing use gaussian method in the filters module. To rotate the image use rotate function under the transform module. To rescale the image use rescale function from the transform module. It can help you perform several operations on images like rotating, resizing, cropping, grayscaling etc.

### thoughts on “Image noise detection python”

• 04.09.2021 at 07:02