Search results
Results from the WOW.Com Content Network
Gamma correction or gamma is a nonlinear operation used to encode and decode luminance or tristimulus values in video or still image systems. [1] Gamma correction is, in the simplest cases, defined by the following power-law expression: =,
SNR is sometimes quantified in decibels (dB) of signal power relative to noise power, though in the imaging field the concept of "power" is sometimes taken to be the power of a voltage signal proportional to optical power; so a 20 dB SNR may mean either 10:1 or 100:1 optical power, depending on which definition is in use.
Image noise can also originate in film grain and in the unavoidable shot noise of an ideal photon detector. Image noise is an undesirable by-product of image capture that obscures the desired information. Typically the term “image noise” is used to refer to noise in 2D images, not 3D images.
Oversharpening, can degrade image quality by causing "halos" to appear near contrast boundaries. Images from many compact digital cameras are sometimes oversharpened to compensate for lower image quality. Noise is a random variation of image density, visible as grain in film and pixel level variations in digital images. It arises from the ...
Noise reduction, the recovery of the original signal from the noise-corrupted one, is a very common goal in the design of signal processing systems, especially filters. The mathematical limits for noise removal are set by information theory .
Image Restoration: Image restoration focuses on solving the problem = + where is the blurry image that should be restored, is the blur kernel, is the additive noise and is the original image we wish to recover. The traditional filter which is used to solve this problem is the Wiener Filter.
Luma is the weighted sum of gamma-compressed R′G′B′ components of a color video—the prime symbols ′ denote gamma compression. The word was proposed to prevent confusion between luma as implemented in video engineering and relative luminance as used in color science (i.e. as defined by CIE ).
Typical values for the PSNR in lossy image and video compression are between 30 and 50 dB, provided the bit depth is 8 bits, where higher is better. The processing quality of 12-bit images is considered high when the PSNR value is 60 dB or higher. [3] [4] For 16-bit data typical values for the PSNR are between 60 and 80 dB.