AI Article Synopsis
- Modern imaging devices create images using electromagnetic or acoustic waves, resulting in "energy" pictures based on the waves' energy.
- These waves also carry "information" characterized by entropy, leading to the development of "information imaging" that offers advantages over traditional methods.
- The most sensitive measure for this information imaging is the joint entropy between the signal and a reference, and studies have shown that using an optimal reference can enhance the reliability of the imaging process.
Article Abstract
Virtually all modern imaging devices collect electromagnetic or acoustic waves and use the energy carried by these waves to determine pixel values to create what is basically an "energy" picture. However, waves also carry "information", as quantified by some form of entropy, and this may also be used to produce an "information" image. Numerous published studies have demonstrated the advantages of entropy, or "information imaging", over conventional methods. The most sensitive information measure appears to be the joint entropy of the collected wave and a reference signal. The sensitivity of repeated experimental observations of a slowly-changing quantity may be defined as the mean variation (, observed change) divided by mean variance (, noise). Wiener integration permits computation of the required mean values and variances as solutions to the heat equation, permitting estimation of their relative magnitudes. There always exists a reference, such that joint entropy has larger variation and smaller variance than the corresponding quantities for signal energy, matching observations of several studies. Moreover, a general prescription for finding an "optimal" reference for the joint entropy emerges, which also has been validated in several studies.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4838411 | PMC |
| http://dx.doi.org/10.3390/e17063518 | DOI Listing |
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