Jove
Visualize
Contact Us

Related Concept Videos

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
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...
Accuracy, limits, and approximation01:28

Accuracy, limits, and approximation

Accuracy, limits, and approximations are common in many fields, especially in engineering calculations. These concepts are imperative for ensuring that a given value is as close as possible to its true value.
Accuracy is defined as the closeness of the measured value to the true or actual value. In engineering mechanics, repeated measurements are taken during theoretical or experimental analyses to ensure that the result is precise and accurate.
The accuracy of any solution is based on the...
Upsampling01:22

Upsampling

Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
Deconvolution01:20

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...

You might also read

Related Articles

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

Sort by
Same author

Denoising: a powerful building block for imaging, inverse problems and machine learning.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2025
Same author

DVMark: A Deep Multiscale Framework for Video Watermarking.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2023
Same author

Mobile Computational Photography: A Tour.

Annual review of vision science·2021
Same author

Better Compression With Deep Pre-Editing.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2021
Same author

Deep K-SVD Denoising.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2021
Same author

Local Kernels that Approximate Bayesian Regularization and Proximal Operators.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2019
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 Experiment Videos

Practical bounds on image denoising: from estimation to information.

Priyam Chatterjee1, Peyman Milanfar

  • 1Department of Electrical Engineering, University ofCalifornia, Santa Cruz, Santa Cruz, CA 95064, USA. priyam@soe.ucsc.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|November 17, 2010
PubMed
Summary

This study develops a new method to estimate image denoising bounds without needing the original clean image. It shows these bounds can be calculated from corrupted images, offering practical insights for image restoration.

Related Experiment Videos

Area of Science:

  • Computer Vision
  • Image Processing
  • Information Theory

Background:

  • Previous work established image denoising bounds using noise-free images.
  • Practical image denoising often lacks access to the ground truth (noise-free) image.

Purpose of the Study:

  • To extend image denoising bound formulation to scenarios without ground truth.
  • To enable estimation of denoising bounds directly from noisy images.

Main Methods:

  • Developed a method to estimate bound parameters (cluster covariances, patch redundancy) from noisy images.
  • Analyzed the interdependence of these parameters within the bounds formulation.
  • Interpreted the bounds and parameters using information-theoretic principles.

Main Results:

  • Demonstrated that key parameters for denoising bounds can be estimated directly from corrupted images.
  • Established an interdependence between cluster covariances and patch redundancy.
  • Provided an information-theoretic interpretation of the denoising bounds.

Conclusions:

  • The proposed method offers a practical approach to assessing image denoising performance without ground truth.
  • The findings contribute to a deeper theoretical understanding of image denoising limits.
  • Experimental validation supports the effectiveness of the developed bounds.