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

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...
Random Error01:04

Random Error

Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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...
Random and Systematic Errors01:20

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
Random and Systematic Errors01:20

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...

You might also read

Related Articles

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

Sort by
Same author

No One-Size-Fits-All Neurons: Task-based Neurons for Artificial Neural Networks.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

SpecEStop: Self-Supervised Hyperspectral Mixed Noise Removal via Deep Spectral Prior.

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

Local and High-Order Consistency Coding and Adaptation for Cross-Hypergraph Node Classification.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Separable Decomposition for Ragged Tensors.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Hyper-Compression: Model Compression via Hyperfunction.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Nonlinear Transformed Low-Rank Quaternion Tensor Total Variation for Multidimensional Color Image Completion.

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

Mask-guided Asymmetric Contrastive and Semantic Alignment for Unsupervised Person Re-Identification.

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

Hyperbolic Cycle Alignment for Infrared-Visible Image Fusion.

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

Learning Gaze Synthesizer via 3D-eye Controlled Diffusion and Cross-domain Feature Alignment.

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

Underlying Semantic Diffusion for Effective and Efficient In-Context Learning.

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

DiffRES: Unleashing Text-to-Image Diffusion Models for Generative Referring Expression Segmentation without Information Leakage.

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

Location Matters: Frequency-Spatial Dual Space Adaptation for Cross-Domain Few-Shot Segmentation.

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

Related Experiment Videos

Multiplicative noise removal via a learned dictionary.

Yu-Mei Huang1, Lionel Moisan, Michael K Ng

  • 1School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China. huangym@lzu.edu.cn

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|June 28, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for multiplicative noise removal in images by learning a dictionary from transformed data. The proposed algorithm enhances image quality and outperforms existing state-of-the-art techniques.

Related Experiment Videos

Area of Science:

  • Image Processing
  • Computer Vision
  • Signal Processing

Background:

  • Multiplicative noise removal is a significant challenge in image processing.
  • Current methods often rely on logarithmic transformation to convert problems into additive denoising.
  • Sparse image representations offer effective solutions for image recovery tasks.

Purpose of the Study:

  • To develop an advanced algorithm for multiplicative noise removal.
  • To leverage sparse representations for improved image denoising.
  • To enhance image quality and accuracy in noisy images.

Main Methods:

  • Learning a dictionary from the logarithmically transformed image data.
  • Utilizing a variational model incorporating the learned dictionary for denoising.
  • Evaluating performance using visual quality, peak signal-to-noise ratio (PSNR), and mean absolute deviation (MAD) error.

Main Results:

  • The proposed algorithm demonstrates superior performance compared to existing state-of-the-art methods.
  • Significant improvements observed in visual quality of denoised images.
  • Quantitative metrics including PSNR and MAD error show enhanced results.

Conclusions:

  • The proposed dictionary learning approach within a variational model is effective for multiplicative noise removal.
  • This method offers a promising advancement in image denoising techniques.
  • The algorithm provides better visual and quantitative outcomes than current approaches.