Truetest™ MURA DETECTION PART I – MOIRE REMOVAL

TrueTest™ MURA DETECTION PART I – MOIRE REMOVAL

• To perform effective Mura analysis algorithms generally must perform a series of steps to accurately and repeatability find Mura • The first step in this process is generally filtering • Filtering can be broken up into three steps: • Down sampling or “Binning” • Median • Low Pass • Other specialized filters, such as Moiré removal, can also be applied

• Down sampling or “Binning” is the simplest filter • Reduces pixel count by a fixed ratio • Make image faster to process but sacrifices detail

• A median filter considers a group of surrounding pixels and replaces each pixel with the median value. • This can be used to repair images that contain bad pixels or random noise.

• All functions can be represented as a superposition of an infinite number of waves at different frequencies, phases and magnitudes. • An image consists of spatial information that can be decomposed into these frequencies using Fourier methods. • If we know these frequencies we can use that information to learn more about the image or perform different kinds of filters on that image. • Fourier theory works in the spatial domain exactly like it works in time domain.

∞ F(ω) = ∫ f (t)e− jωt dt −∞ • A Fourier transform converts between frequency domain and spatial or time domain. • FFT stands for “Fast Fourier Transform” and is a very powerful tool for image processing and analysis. • Often used in the time domain for radio frequency processing, it can also be used in the spatial domain for image processing.

• A low pass filter removes high frequency components from a signal, leaving only the lower frequencies behind. • This means any fast changes (high frequencies) will be removed. • This is a powerful tool for image processing, it can be used to remove artifacts from various processes, such as halftone dots from newsprint or lines from images stitched together.

• Original Image:

• Moiré is an effect of aliasing between the pixels of a CCD array and the pixels of a display being imaged upon it. • Because there are small gaps between the pixels of both the display and the sensor, those gaps can be imaged on the pixels of the sensors and appear as lines of various frequencies in the image.

• Because Moiré appears at regular frequencies it can be isolated and removed in the frequency domain. • By using an FFT, we can automatically remove Moiré artifacts with our image processing software.

• Mura comes in many types and to effectively find it with an automated algorithm images must be prepared for this process. • Filtering can improve processing speed and repeatability. • Frequency filtering can not only clean up an image without losing potentially important information, it can actually be used to identify the types of defects we’re looking for. • It’s not just smoothing, smoothing will destroy information that is useful for display quality inspection, such as pixel defects.

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