Related Experiment Video
Updated: Jan 24, 2026

Reconstruction of Single-Cell Innate Fluorescence Signatures by Confocal Microscopy
Published on: May 27, 2020
A Feature based Reconstruction Model for Fluorescence Microscopy Image Denoising
Suman Kumar Maji1, Hussein Yahia2
1Department of Computer Science & Engineering, Indian Institute of Technology Patna, Patna, 800 013, India. smaji@iitp.ac.in.
Abstract:
The advent of Fluorescence Microscopy over the last few years have dramatically improved the problem of visualization and tracking of specific cellular objects for biological inference. But like any other imaging system, fluorescence microscopy has its own limitations. The resultant images suffer from the effect of noise due to both signal dependent and signal independent factors, thereby limiting the possibility of biological inferencing. Denoising is a class of image processing algorithms that aim to remove noise from acquired images and has gained wide attention in the field of fluorescence microscopy image restoration. In this paper, we propose an image denoising algorithm based on the concept of feature extraction through multifractal decomposition and then estimate a noise free image from the gradients restricted to these features. Experimental results over simulated and real fluorescence microscopy data prove the merit of the proposed approach, both visually and quantitatively.
Related Concept Videos
Confocal Fluorescence Microscopy
Super-resolution Fluorescence Microscopy
Total Internal Reflection Fluorescence Microscopy
Imaging Biological Samples with Optical Microscopy
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
Reconstruction of Signal using Interpolation
COPD: Pathogenesis and Clinical Features
The primary cause for the onset of COPD is cigarette smoking and exposure to air pollution. These hazardous factors initiate a chain reaction within the lungs, resulting in chronic inflammation, damage to the airways, and a...

