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Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

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Published on: July 11, 2025

Removing artefacts from color and contrast modifications.

Julien Rabin¹, Julie Delon, Yann Gousseau

¹CMLA, École Normale Supérieure de Cachan, Cachan, France. rabin@cmla.ens-cachan.fr

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|April 22, 2011

Summary

This study introduces a new digital image filtering method to remove artifacts and preserve details during radiometric corrections. The technique regularizes the transportation map, improving image quality for various modifications.

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Area of Science:

Digital Image Processing
Computer Vision
Image Analysis

Background:

Classical digital image modification methods often introduce artifacts or degrade image details and textures.
Radiometric corrections, such as contrast equalization and color enhancement, are crucial for image analysis but can be prone to these drawbacks.

Purpose of the Study:

To propose a generic filtering method for suppressing artifacts in digital images after radiometric correction.
To preserve image details and textures during the correction process.

Main Methods:

The proposed method utilizes the difference between the original and radiometrically corrected image, termed the 'transportation map'.
Artefacts are identified as spatial irregularities within this transportation map.
A nonlocal Yaroslavsky filter is employed to regularize the transportation map, thereby suppressing artifacts.

Main Results:

The filtering method effectively suppresses artifacts introduced by radiometric modifications.
Image details and textures are preserved, unlike in classical methods.
The approach demonstrates efficiency across various radiometric modifications, including contrast equalization, midway histogram, color enhancement, and color transfer.

Conclusions:

The proposed generic filtering method offers a robust solution for artifact suppression in digital image radiometric correction.
It successfully balances artifact removal with the preservation of essential image details and textures.
This technique provides a valuable advancement for digital image processing applications requiring high fidelity.