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Image Editing versus Image Processing and Low Frequency Flat Field Correction

Posted by James Hendrix on Thu, Oct 12, 2017

Image editing vs image processing

Image editing is often thought of as image manipulation, a word with nasty connotations. Image editing is about changing the image in some way, you could say manipulating it. However, “I photoshopped a picture from my fishing trip,” conjures up images of making the minnow you caught into a prize marlin when perhaps all you did was heighten the color saturation or increase the contrast. In contrast, image processing is about bringing the information out of the noise. Image Processing is the science of image enhancement and includes photo editing. Both enhance the image and both can be used to bring out otherwise hidden information but image processing is all about bringing the information out of an otherwise noisy scene. Image processing is generally about applying equations and algorithms to images to make data stand out, be easier to detect. Image processing is what Adimec does to generate True Accurate Images and remove system defects and artifacts so that the scene image is accurate.

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Topics: Image Quality Improvements, Machine vision

Throwback Thursday – Video Enhancement Mode – advancements in image processing

Posted by Benny Koene on Thu, Jun 1, 2017

With the ever decreasing size of computer chips and the large availability of FPGAs it is relatively easy to integrate image processing steps within the camera module. Take for example our TMX series which is designed for outdoor observation/viewing applications. These camears are provided with a Video Enhancement Mode to optimize the video signal for maximum image contrast which is especially useful in unfavorable weather conditions. However it hasn’t always been that easy to integrate image processing functionality inside a camera.

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Topics: Image Quality Improvements, Global security, Throwback Thursday

Practical tips for using software to analyze image quality

Posted by Benny Koene on Thu, Jan 26, 2017

Practical tips for using software to analyze image quality

Next to writing blog’s I am also part of the support team within Adimec. To support customers the support team frequently has to analyze image quality. Image averages, standard deviations, or histograms are frequently used image properties that help us in our analysis but also cross sections of images come in handy.

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Topics: Applications, Vision System Optimization, Image Quality Improvements

How to improve image quality under low light circumstances

Posted by Benny Koene on Wed, Dec 21, 2016

As today is the shortest day of the year (at least in the northern hemisphere) and the largest part of the day is dark, we though it a good moment to give you a few ideas on how to optimize image quality in low light conditions. Due to the simple fact that you need light to obtain an image with a visible camera, low light conditions are by definition challenging. However, even with low light you can take measures to obtain the best possible image result.

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Topics: Applications, Image Quality Improvements

Get better image information from machine vision cameras – removing artifacts with flat field correction

Posted by Gretchen Alper on Thu, Jun 23, 2016

Flat Field Correction in Machine Vision Cameras

We recently published a series of articles about using Flat Field Correction in high-resolution Machine Vision cameras. 

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Topics: Image Quality Improvements

How to use Flat Field Correction in practice?

Posted by Benny Koene on Thu, Jun 16, 2016

Flat Field Correction (FFC) in cameras for Machine Vision Part 3

As we have discussed in previous articles, elsewhere on our blog, various types of flat field correction (FFC) exist. Depending on the variant, a flat field correction corrects for dark signal non-uniformities (DSNU), photo response non-uniformities (PRNU) and/or artifacts caused by the illumination and illumination optics. In this blog post we will briefly explain the required steps to make flat field correction work in practice.

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Topics: Image Quality Improvements

Which types of flat field corrections exist and why it matters for high resolution cameras?

Posted by Benny Koene on Thu, Jun 9, 2016

Flat Field Correction (FFC) in cameras for Machine Vision Part 2

Flat field correction is a widely used term as a lot of industrial and machine vision cameras have some form of correction algorithms to overcome image artifacts. However not all forms of flat field correction are the same and with the growing amount of pixels on a sensor, the variations in methods of how a flat field is achieved has only increased. In this article we will clarify the existing correction mechanisms.

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Topics: Image Quality Improvements

How to remove light source and illumination optics artifacts from your camera image

Posted by Benny Koene on Mon, Jun 6, 2016

Flat Field Correction (FFC) in cameras for Machine Vision Part 1

This is the first in a series of articles about using Flat Field Correction in high-resolution Machine Vision cameras.

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Topics: Image Quality Improvements

How to interpret the Dynamic Range and Signal to Noise Ratio (SNR) in image sensor and industrial camera specifications

Posted by Gretchen Alper on Wed, May 18, 2016

With machine vision applications, some of the most important specifications beyond resolution and frame speed to determine whether the camera meet the measurement requirements, are full well capacity, Signal to Noise Ratio (SNR), and dynamic range (DR) specifications.  Interpreting these values from specification sheets can be challenging though.  The full well capacity and SNR definitions that are used for the image sensor do not always match those that are used in the specification sheets of the resulting industrial camera. Dynamic range in particular can be confusing as there are different ways to calculate it. 

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Topics: Image Quality Improvements

Reducing noise and increasing camera frame rate through binning - on sensor binning versus digital binning

Posted by Gretchen Alper on Tue, May 10, 2016

There are always small changes (at no additional product cost) that can be made to increase the performance of your machine vision camera and thus to your overall inspection or metrology system.  Perhaps there are low light levels in the system and you need to improve image quality. Binning which is adding the charge of 2 or more pixels together can both increase signal to noise ratio (SNR) and frame rate. Higher signal-to-noise ratio is achieved due to reduced read noise contributions and adding  signals (pixels) together. By adding pixels together the noise component will be reduced due to averaging. Because fewer pixels are processed with binning, a higher camera frame rate can be achieved to increase the system throughput. 

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Topics: Image Quality Improvements