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Contrast-to-noise ratio (CNR) [1] is a measure used to determine image quality. CNR is similar to the metric signal-to-noise ratio (SNR), but subtracts a term before taking the ratio. This is important when there is a significant bias in an image, such as from haze. [2] As can be seen in the picture at right, the intensity is rather high even though the features of the image are washed out by the haze. Thus this image may have a high SNR metric, but will have a low CNR metric.
One way to define contrast-to-noise ratio is: [3] [4]
where SA and SB are signal intensities for signal producing structures A and B in the region of interest and σo is the standard deviation of the pure image noise.
Magnetic resonance imaging (MRI) is a medical imaging technique used in radiology to form pictures of the anatomy and the physiological processes of the body. MRI scanners use strong magnetic fields, magnetic field gradients, and radio waves to generate images of the organs in the body. MRI does not involve X-rays or the use of ionizing radiation, which distinguishes it from computed tomography (CT) and positron emission tomography (PET) scans. MRI is a medical application of nuclear magnetic resonance (NMR) which can also be used for imaging in other NMR applications, such as NMR spectroscopy.
Noise figure (NF) and noise factor (F) are figures of merit that indicate degradation of the signal-to-noise ratio (SNR) that is caused by components in a signal chain. These figures of merit are used to evaluate the performance of an amplifier or a radio receiver, with lower values indicating better performance.
Signal-to-noise ratio is a measure used in science and engineering that compares the level of a desired signal to the level of background noise. SNR is defined as the ratio of signal power to noise power, often expressed in decibels. A ratio higher than 1:1 indicates more signal than noise.
Image registration is the process of transforming different sets of data into one coordinate system. Data may be multiple photographs, data from different sensors, times, depths, or viewpoints. It is used in computer vision, medical imaging, military automatic target recognition, and compiling and analyzing images and data from satellites. Registration is necessary in order to be able to compare or integrate the data obtained from these different measurements.
Peak signal-to-noise ratio (PSNR) is an engineering term for the ratio between the maximum possible power of a signal and the power of corrupting noise that affects the fidelity of its representation. Because many signals have a very wide dynamic range, PSNR is usually expressed as a logarithmic quantity using the decibel scale.
In telecommunications, the carrier-to-noise ratio, often written CNR or C/N, is the signal-to-noise ratio (SNR) of a modulated signal. The term is used to distinguish the CNR of the radio frequency passband signal from the SNR of an analog base band message signal after demodulation. For example, with FM radio, the strength of the 100 MHz carrier with modulations would be considered for CNR, whereas the audio frequency analogue message signal would be for SNR; in each case, compared to the apparent noise. If this distinction is not necessary, the term SNR is often used instead of CNR, with the same definition.
Contrast resolution is the ability to distinguish between differences in intensity in an image. The measure is used in medical imaging to quantify the quality of acquired images. It is a difficult quantity to define because it depends on the human observer as much as the quality of the actual image. For example, the size of a feature affects how easily it is detected by the observer.
Compressed sensing is a signal processing technique for efficiently acquiring and reconstructing a signal, by finding solutions to underdetermined linear systems. This is based on the principle that, through optimization, the sparsity of a signal can be exploited to recover it from far fewer samples than required by the Nyquist–Shannon sampling theorem. There are two conditions under which recovery is possible. The first one is sparsity, which requires the signal to be sparse in some domain. The second one is incoherence, which is applied through the isometric property, which is sufficient for sparse signals.
Fellgett's advantage or the multiplex advantage is an improvement in signal-to-noise ratio (SNR) that is gained when taking multiplexed measurements rather than direct measurements. The name is derived from P. B. Fellgett, who first made the observation as part of his PhD. When measuring a signal whose noise is dominated by detector noise, a multiplexed measurement such as the signal generated by a Fourier transform spectrometer can produce a relative improvement in SNR, compared to an equivalent scanning monochromator, of the order of the square root of m, where m is the number of sample points comprising the spectrum.
Signal-to-noise ratio (SNR) is used in imaging to characterize image quality. The sensitivity of a imaging system is typically described in the terms of the signal level that yields a threshold level of SNR.
Stochastic resonance is a phenomenon that occurs in a threshold measurement system when an appropriate measure of information transfer is maximized in the presence of a non-zero level of stochastic input noise thereby lowering the response threshold; the system resonates at a particular noise level.
Quantum microscopy allows microscopic properties of matter and quantum particles to be measured and imaged. Various types of microscopy use quantum principles. The first microscope to do so was the scanning tunneling microscope, which paved the way for development of the photoionization microscope and the quantum entanglement microscope.
Computational imaging is the process of indirectly forming images from measurements using algorithms that rely on a significant amount of computing. In contrast to traditional imaging, computational imaging systems involve a tight integration of the sensing system and the computation in order to form the images of interest. The ubiquitous availability of fast computing platforms, the advances in algorithms and modern sensing hardware is resulting in imaging systems with significantly enhanced capabilities. Computational Imaging systems cover a broad range of applications include computational microscopy, tomographic imaging, MRI, ultrasound imaging, computational photography, Synthetic Aperture Radar (SAR), seismic imaging etc. The integration of the sensing and the computation in computational imaging systems allows for accessing information which was otherwise not possible. For example:
Photopyroelectric As known that Photopyroelectric can be regarded as –Photo +Pyroelectric,which means any optical systems using a pyroelectric detector or imaging system, In addition, pyroelectricity could be depicted as the capability of the components formulating the transient voltage when heated or cooled. Once the temperature on which they depend changes, the position of the atom will change slightly in the crystal structure. This process of change can also be referred to as the polarization of the material. As a result, the voltage across the crystal will be triggered by this change in polarization. To further explain, when the temperature in the engine is kept constant for a period of time, the voltage in the photovoltage will gradually disappear due to the leakage current. In this sense, leakage is mainly caused by several ways, for example, electrons going through the crystal, ions going through the air, or current leaking through a voltmeter connected to the crystal.
Coherent Raman scattering (CRS) microscopy is a multi-photon microscopy technique based on Raman-active vibrational modes of molecules. The two major techniques in CRS microscopy are stimulated Raman scattering (SRS) and coherent anti-Stokes Raman scattering (CARS). SRS and CARS were theoretically predicted and experimentally realized in the 1960s. In 1982 the first CARS microscope was demonstrated. In 1999, CARS microscopy using a collinear geometry and high numerical aperture objective were developed in Xiaoliang Sunney Xie's lab at Harvard University. This advancement made the technique more compatible with modern laser scanning microscopes. Since then, CRS's popularity in biomedical research started to grow. CRS is mainly used to image lipid, protein, and other bio-molecules in live or fixed cells or tissues without labeling or staining. CRS can also be used to image samples labeled with Raman tags, which can avoid interference from other molecules and normally allows for stronger CRS signals than would normally be obtained for common biomolecules. CRS also finds application in other fields, such as material science and environmental science.
Spectral imaging is an umbrella term for energy-resolved X-ray imaging in medicine. The technique makes use of the energy dependence of X-ray attenuation to either increase the contrast-to-noise ratio, or to provide quantitative image data and reduce image artefacts by so-called material decomposition. Dual-energy imaging, i.e. imaging at two energy levels, is a special case of spectral imaging and is still the most widely used terminology, but the terms "spectral imaging" and "spectral CT" have been coined to acknowledge the fact that photon-counting detectors have the potential for measurements at a larger number of energy levels.
Photon-counting mammography was introduced commercially in 2003 and was the first widely available application of photon-counting detector technology in medical x-ray imaging. Photon-counting mammography improves dose efficiency compared to conventional technologies, and enables spectral imaging.
4D scanning transmission electron microscopy is a subset of scanning transmission electron microscopy (STEM) which utilizes a pixelated electron detector to capture a convergent beam electron diffraction (CBED) pattern at each scan location. This technique captures a 2 dimensional reciprocal space image associated with each scan point as the beam rasters across a 2 dimensional region in real space, hence the name 4D STEM. Its development was enabled by evolution in STEM detectors and improvements computational power. The technique has applications in visual diffraction imaging, phase orientation and strain mapping, phase contrast analysis, among others.
Ultrasound-switchable fluorescence (USF) imaging is a deep optics imaging technique. In last few decades, fluorescence microscopy has been highly developed to image biological samples and live tissues. However, due to light scattering, fluorescence microscopy is limited to shallow tissues. Since fluorescence is characterized by high contrast, high sensitivity, and low cost which is crucial to investigate deep tissue information, developing fluorescence imaging technique with high depth-to-resolution ratio would be promising.. Recently, ultrasound-switchable fluorescence imaging has been developed to achieve high signal-to-noise ratio (SNR) and high spatial resolution imaging without sacrificing image depth.
Dual-axis optical coherence tomography (DA-OCT) is an imaging modality that is based on the principles of optical coherence tomography (OCT). These techniques are largely used for medical imaging. OCT is non-invasive and non-contact. It allows for real-time, in situ imaging and provides high image resolution. OCT is analogous to ultrasound but relies on light waves, which makes it faster than ultrasound. In general, OCT has proven to be compact and portable. It is compatible with arterial catheters and endoscopes, which helps diagnose diseases within long internal cavities, including the esophagus and coronary arteries.