This paper introduces an universal and structure-preserving regularization term, called quantile sparse image (QuaSI) prior. The prior is suitable for denoising images from various medical image modalities. We demonstrate its effectivness on volumetric optical coherence tomography (OCT) and computed ...
This underestimation of the background at the alleys can be solved by using a structuring element (B) with a variable size such that the number of background pixels inside the structuring element is independent of where on the image it is placed. Alternatively, the quantile (q) may be ...
Optical coherence tomography (OCT) enables high-resolution and non-invasive 3D imaging of the human retina but is inherently impaired by speckle noise. This paper introduces a spatio-temporal denoising algorithm for OCT data on a B-scan level using a novel quantile sparse image (QuaSI) prior. T...
Quantifying uncertainty in brain-predicted age using scalar-on-image quantile regressiondoi:10.1101/853341Marco PalmaShahin TavakoliJulia BrettschneiderThomas E. NicholsCold Spring Harbor Laboratory
Color images showed some jitters in boundary area of airplane. These jitters came from low resolution of multi spectral image. In this study, we proposed an algorithm to eliminate jaggy boundary of airplane by using quantile.Tsukasa HosomuraAsian conference on remote sensingACRS...
Linearly quantile separated histogram equalizationRetinal imageRetinal imaging is used to diagnose common eye diseases. But retinal images that suffer from image blurring, uneven illumination and low contrast become useless for further diagnosis by automated systems. In this work, we have proposed a new...
No Reference Image Quality Assessment of Artificial Feed-Forward Network Model using Quantile Regressiondoi:10.2139/ssrn.3883108Peak signal to noise ratioimage quality assessmentcomputational complexityartificial deep structure learning frameworkAssessing image quality is significant for most issues in image ...
image processinggamma distributionhyperspectralConventional, regression-based methods of inferring depth from passive optical image data undermine the advantages of remote sensing for characterizing river systems. This study introduces and evaluates a more flexible framework, Image-to-Depth Quantile ...
Image pConventional, regression-based methods of inferring depth from passive optical image data undermine the advantages of remote sensing for characterizing river systems. This study introduces and evaluates a more flexible framework, Image-to-Depth Quantile Transformation (IDQT), that involves linking...
Full size image Figure 5 Block structure in morphological-filtered images compared to quantile filters. Background estimates in the red channel on Slide 1 (rows 1800, ..., 2300 and columns 500, ..., 1000) using morphological openingγ50 × 50(left) and quantile openingγ50 × 50,{0.2}(...