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Related Concept Videos

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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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...

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Related Experiment Video

Updated: May 19, 2026

Troubleshooting and Quality Assurance in Hyperpolarized Xenon Magnetic Resonance Imaging: Tools for High-Quality Image Acquisition
09:55

Troubleshooting and Quality Assurance in Hyperpolarized Xenon Magnetic Resonance Imaging: Tools for High-Quality Image Acquisition

Published on: January 5, 2024

No-reference image quality assessment in the spatial domain.

Anish Mittal1, Anush Krishna Moorthy, Alan Conrad Bovik

  • 1Laboratory for Image and Video Engineering, Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, TX 78712, USA. mittal.anish@gmail.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|August 23, 2012
PubMed
Summary
This summary is machine-generated.

We introduce BRISQUE, a novel blind image quality assessment model. This distortion-generic approach uses natural scene statistics for efficient and accurate image quality evaluation without reference images.

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Last Updated: May 19, 2026

Troubleshooting and Quality Assurance in Hyperpolarized Xenon Magnetic Resonance Imaging: Tools for High-Quality Image Acquisition
09:55

Troubleshooting and Quality Assurance in Hyperpolarized Xenon Magnetic Resonance Imaging: Tools for High-Quality Image Acquisition

Published on: January 5, 2024

Area of Science:

  • Computer Vision
  • Image Processing
  • Signal Processing

Background:

  • Blind/No-Reference (NR) Image Quality Assessment (IQA) is crucial for evaluating image fidelity without original data.
  • Existing NR IQA models often rely on distortion-specific features, limiting their generalizability.
  • Natural Scene Statistics (NSS) provide a powerful framework for analyzing image structure and detecting deviations from naturalness.

Purpose of the Study:

  • To develop a distortion-generic, no-reference image quality assessment model operating in the spatial domain.
  • To propose a computationally efficient model for real-time applications.
  • To demonstrate the utility of the model in blind image denoising tasks.

Main Methods:

  • Developed the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) model.
  • Utilized scene statistics of locally normalized luminance coefficients as features.
  • Employed the empirical distribution of locally normalized luminances and their products under an NSS model.
  • Avoided transformations to other domains like DCT or wavelet.

Main Results:

  • BRISQUE achieves statistically superior performance compared to Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM).
  • The model demonstrates high competitiveness against existing distortion-generic NR IQA algorithms.
  • BRISQUE exhibits very low computational complexity, suitable for real-time applications.
  • Augmenting nonblind image denoising with BRISQUE improved performance over state-of-the-art methods.

Conclusions:

  • BRISQUE offers a simple yet effective approach to distortion-generic NR IQA using spatial domain NSS.
  • The model's efficiency and performance make it suitable for practical applications, including blind image denoising.
  • BRISQUE features can also be leveraged for distortion identification.