..., 2300 and columns 500, ..., 1000) using morphological openingγ50 × 50(left) and quantile openingγ50 × 50,{0.2}(right). The quantile filter gives a smoother image. Gray scales are not comparable.
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 ...
Quantifying uncertainty in brain-predicted age using scalar-on-image quantile regressiondoi:10.1101/853341Marco PalmaShahin TavakoliJulia BrettschneiderThomas E. NicholsCold Spring Harbor Laboratory
Noise model mismatch would likely result in image denoising that undersmoothes real sensor data.Zhang, JiachaoUniversity of Dayton, OH USAHirakawa, KeigoJin, Xiaodan电气工程Zhang, J., Hirakawa, K. & Jin, X. Quantile analysis of image sensor noise distribution. IEEE International Conference on ...
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...
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 ...
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 ...
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...
J., 2016. Inferring river bathymetry via Image- toDepth Quantile Transformation (IDQT). Water Resources Research 52(5), pp. 3722-3741.Legleiter, C.J. Inferring river bathymetry via Image-to-Depth Quantile Transformation (IDQT). Water Resour. Res. 2016, 52, 3722-3741. [CrossRef]...
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 ...