Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next sampling...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
Linearization and Approximation01:26

Linearization and Approximation

Linearization is a mathematical technique used to approximate complex, nonlinear functions with simpler linear models in the vicinity of a chosen reference point. The method is based on the idea that, although a function may be difficult to evaluate exactly, its behavior near a specific input value can often be closely approximated by the tangent line at that point. This approach is particularly useful when small deviations from a known value are involved.Consider the square root function, for...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Thymus-expressed chemokine promotes survival of PC12 cells via PI3K pathway.

Neurochemistry international·2011
Same author

Retinoic acid signaling sequentially controls visceral and heart laterality in zebrafish.

The Journal of biological chemistry·2011
Same author

A Drosophila model of the neurodegenerative disease SCA17 reveals a role of RBP-J/Su(H) in modulating the pathological outcome.

Human molecular genetics·2011
Same author

Overexpression and small molecule-triggered downregulation of CIP2A in lung cancer.

PloS one·2011
Same author

Antibiofouling hybrid dendritic Boltorn/star PEG thiol-ene cross-linked networks.

ACS applied materials & interfaces·2011
Same author

An absolute test for axicon surfaces.

Optics letters·2011
Same journal

Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

GoP-based Quality Enhancement on Video Compression.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Align then Tensorize: Multi-Level Consistent Anchor Graph Learning for Scalable Multi-View Clustering.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Beyond Fidelity: Diverse Image Synthesis via Retrieval-Augmented Diffusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
See all related articles

Related Experiment Videos

A multiplicative iterative algorithm for box-constrained penalized likelihood image restoration.

Raymond H Chan1, Jun Ma

  • 1Department of Statistics, Macquarie University, Sydney, N.S.W. 2109, Australia. jun.ma@mq.edu.au

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|March 1, 2012
PubMed
Summary
This summary is machine-generated.

A new box-constrained multiplicative iterative (BCMI) algorithm offers efficient image restoration. This method simplifies pixelwise updates for improved box-constrained image processing, handling various noise types effectively.

Related Experiment Videos

Area of Science:

  • Digital Image Processing
  • Computational Imaging
  • Optimization Algorithms

Background:

  • Image restoration is computationally demanding, requiring determination of numerous pixel values.
  • Pixel values are constrained within a dynamic range, necessitating box constraints.
  • Traditional gradient projection methods for box constraints can be slow and inefficient.

Purpose of the Study:

  • To develop a novel algorithm for box-constrained image restoration.
  • To address the limitations of existing methods in terms of convergence and computational complexity.
  • To provide an efficient solution for restoring images with various noise types.

Main Methods:

  • Development of a new box-constrained multiplicative iterative (BCMI) algorithm.
  • BCMI algorithm performs pixelwise updates, avoiding matrix inversion.
  • Convergence of the BCMI algorithm is mathematically proven.

Main Results:

  • The BCMI algorithm demonstrates efficient pixelwise updates for image restoration.
  • The method successfully imposes box constraints on pixel values.
  • Applied to total variation image restoration, it effectively handles Poisson, Gaussian, and salt-and-pepper noise.

Conclusions:

  • The BCMI algorithm provides an effective and computationally efficient approach to box-constrained image restoration.
  • It offers an improvement over traditional gradient projection methods.
  • The algorithm is robust for diverse noise conditions in image restoration tasks.