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Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
Downsampling01:20

Downsampling

When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
Imaging Studies for Cardiovascular System IV: CMRI01:21

Imaging Studies for Cardiovascular System IV: CMRI

Cardiovascular magnetic resonance imaging, or CMRI, is a non-invasive diagnostic test that employs a magnetic field and radiofrequency waves to create precise images of the heart and arteries. It provides comprehensive information about cardiac anatomy, function, perfusion, and tissue characterization without ionizing radiation.IndicationsCMRI diagnoses various heart conditions, including tissue damage from heart attacks, ischemic heart disease, myocarditis, aortic issues (tears, aneurysms,...
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...

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Related Experiment Video

Updated: Jun 4, 2026

Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

Compressive rendering: a rendering application of compressed sensing.

Pradeep Sen1, Soheil Darabi

  • 1UNM Advanced Graphics Lab, Department ofElectrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA. psen@ece.unm.edu

IEEE Transactions on Visualization and Computer Graphics
|February 12, 2011
PubMed
Summary
This summary is machine-generated.

Compressed sensing (CS) accelerates rendering by exploiting image sparsity in the wavelet domain. This method reconstructs high-quality images using significantly fewer samples than traditional rendering techniques.

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Last Updated: Jun 4, 2026

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Area of Science:

  • Computer Graphics
  • Signal Processing
  • Applied Mathematics

Background:

  • Compressed sensing (CS) theory enables signal reconstruction from limited measurements.
  • CS applications in computer graphics have primarily focused on light transport acquisition.
  • Ray-traced rendering traditionally requires dense sampling for accurate image generation.

Purpose of the Study:

  • To introduce a novel application of compressed sensing for accelerating ray-traced rendering.
  • To leverage the sparsity of images in the wavelet domain for efficient rendering.
  • To demonstrate improved rendering performance compared to conventional methods.

Main Methods:

  • Ray-tracing a subset of pixel samples in the spatial domain.
  • Employing a greedy compressed sensing algorithm to estimate the wavelet transform of the image.
  • Reconstructing the final image via the inverse wavelet transform.

Main Results:

  • Achieved high-quality images using approximately 75% of pixel samples.
  • Demonstrated superior performance over interpolation and inpainting for missing pixel data.
  • The algorithm's image-space operation makes it independent of scene complexity.

Conclusions:

  • Compressed sensing offers an effective approach to accelerate ray-traced rendering.
  • Wavelet domain sparsity allows for significant reduction in required pixel samples.
  • This method provides a robust and efficient alternative for image reconstruction in rendering.