![spectrophotometer artifact meaning spectrophotometer artifact meaning](http://www.fsw.cc/wp-content/uploads/2017/08/pexels-photo-287005-1024x683.jpeg)
The dictionary is a set of elementary atoms and can be used to decompose signal. It assumes that each signal can be represented by a linear combination of sparse elements from a pre-specified dictionary. For this OCT image inpainting caused by saturation artifacts, we formulate it as a dictionary learning problem in sparse representation. However, this model involves multiple parameters, and the performance is not satisfactory. An Euler’s elastica variation image inpainting model can be used to reconstruct the distorted image by minimizing the Euler’s elastica energy function. There are multiple methods to address such inpainting problem. We can formulate the correction of saturation artifacts as an inpainting problem, which reconstructs missing values in OCT images from a few known measurements.
![spectrophotometer artifact meaning spectrophotometer artifact meaning](https://www.lr-test.com/wp-content/uploads/sites/103/2020/12/11-1.jpg)
Sparse representation is related to the compressed sensing (CS) theory if a signal is considered sparse or compressive, it can be faithfully reconstructed by exploiting a small amount of measurements, which is much less than the one suggested by Shannon’s sampling theorem. As above interpolation methods produce less satisfactory outcomes when dealing with dense saturation artifacts, we seek to solve this problem via algorithmic efforts, sparse representation. Moreover, hardware-based methods introduce additional cost to the implementation and lack adaptability in practical applications. Because the ratio of signal on the two channels is non-linear across spectral domain and is roughly estimated, the compensation may lead to high error when the reference signal is highly out-of-focus. To improve the hardware design, a two-channel detector system was proposed to compensate the saturated points using the signal from the channel in which the signal was not saturated in. They compensated saturated points of one line using the signal from the other line. proposed an imaging acquisition process using a dual-line CCD as the detector. The distant region (low-SNR) was interpolated by local polynomial model which takes the detected surface as reference, therefore it increased speckle noise pattern. In, local polynomial model was employed to interpolate distant artifacts to improve the segmentation accuracy of corneal OCT images, but the precise estimation can be obtained only for central artifact region. However, the performance of such interpolation-based method will degrade when A-lines are densely saturated. Linear interpolation was used in to reduce saturation artifacts. To reduce the impact of saturation artifacts, various methods have been proposed via improving current software, , refined image acquisition protocols, , or innovative hardware design. Importantly, the existence of saturation artifacts could hinder the in-depth analysis of OCT images, such as segmentation.Įxamples of saturation artifacts in OCT images: (a) B-scan from onion sample (b) corresponding saturated spectrum. Those artifacts, featured by streaking patterns, are often caused by specular reflections from the interfaces. If the detected signal’s fluctuation exceeds the dynamic range of the CCD, it will give rise to saturation artifacts that is manifested by extraordinarily bright A-lines in the OCT images as shown in Fig. SD-OCT suffers from saturation artifacts. A reflectivity profile is then reconstructed along the axial direction, the direction that is parallel to the light propagation, in the form of axial line (A-line). Take spectral-domain OCT (SD-OCT) as an example, it uses a linear charge-coupled device (CCD) camera as detector to measure spectral interferogram. Saturation artifacts are commonly observed in optical coherence tomography (OCT), which could degrade the segmentation accuracy and lead to errors in derived clinic parameters.