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Updated: Apr 3, 2026

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

Published on: May 24, 2022

Nonlinear Image Representation Using Divisive Normalization.

Siwei Lyu¹, Eero P Simoncelli¹

¹Howard Hughes Medical Institute, and Center for Neuroscience, New York University.

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition

|October 28, 2014

Summary

This summary is machine-generated.

This study introduces a new nonlinear image representation using divisive normalization. This method enhances image contrast and statistical properties for biological visual systems.

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Area of Science:

Computer Vision
Image Processing
Computational Neuroscience

Background:

Current image representations often fail to capture the statistical properties of natural images.
Biological visual systems exhibit complex, nonlinear processing of visual information.

Purpose of the Study:

To develop a nonlinear image representation that aligns with statistical image properties and biological visual perception.
To introduce a computationally efficient and invertible divisive normalization transform for image analysis.

Main Methods:

Image decomposition using a multi-scale oriented representation.
Modeling coefficient dependencies with Student's t-distribution.
Applying divisive normalization to reduce statistical dependencies.

Main Results:

Demonstrated reduction of statistical dependencies in image coefficients.
Developed an efficient iterative algorithm for the invertible divisive normalization transform.
Showcased robustness to added noise and effectiveness in image contrast enhancement.

Conclusions:

The proposed divisive normalization transform offers a powerful tool for image representation.
This method bridges the gap between statistical image modeling and biological visual processing.
The transform provides significant advantages for image analysis and enhancement tasks.