Abstract: MRI is increasingly used to assess, diagnose, and plan treatment for various diseases. Physicians and automated systems can benefit from MR pulse sequences that show tissue in different contrasts in a single scan. However,…
Abstract: Synthetic CT images from modified Dixon (mDixon) MR data are proposed. Attenuation correction (AC) is performed on abdomen and pelvis PET data using synthetic CT. AC is needed in PET/MR systems to be quantitatively…
Abstract: Grayscale image processing with low-rank matrix approximation (LRMA) has been successful. LRMA restores color images using the monochromatic or concatenation models. These two schemes may not maximize RGB channel correlation. A new low-rank quaternion…
Abstract: Atmospheric particles scatter light, reducing the visibility of images taken in poor weather. SID methods restore visibility from hazy images. Its ill-posedness makes the SID difficult. The atmospheric scattering model (ATSM) solves SID problems….
Abstract: Panorama stitching research today focuses on accurate and efficient image alignment. This image processing project proposes a triangular facet approximation-based local-adaptive image alignment method that directly manipulates matching data in camera coordinates and outperforms…
Abstract: Classic image processing problems include lossy image compression (LIC), which uses inexact approximations to compress images. By learning an encoder-quantizer-decoder network from a lot of data, deep convolutional neural networks (CNNs) have produced interesting…
Abstract: Deep convolutional neural network-based image super-resolution (SR) models are better at recovering high-resolution (HR) images from predefined downscaling methods. This image processing projects proposes a content adaptive resampler (CAR)-based learned image downscaling method that…
Abstract: This image processing projects introduces linear compressing based skip-connecting network (LCSCNet) for image super-resolution. In LCSCNet, a linear compressing layer connects former feature maps and distinguishes them from newly explored feature maps, unlike ResNet…
Abstract: Kinetic modeling of in vivo pyruvate-to-lactate conversion is essential for studying Warburg effect-modified cancer metabolism. Non-invasive metabolic flux detection may improve prognosis and treatment monitoring. Read also Image Processing Projects Hyperpolarized [1–13-C] pyruvate was…
Abstract: This Digital Image Processing Project introduces a novel and effective image prior, gamma correction prior (GCP), which leads to an efficient image dehazing method, IDGCP. The proposed IDGCP has the following steps. The proposed…
Abstract: Hazy color images have distorted color contrast fields, making grayscale conversion difficult. This Image Processing Project proposes a new decolorization algorithm to convert hazy images into distortion-recovered grayscale images. The CIELab color space relationship…
Abstract: This Digital Image Processing Project introduces the rank residual constraint (RRC) model for rank minimization. We minimize the rank residual to progressively approximate the underlying low-rank matrix, unlike nuclear norm minimization (NNM) and weighted…
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