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Multiplex Chemical Imaging Based on Broadband Stimulated Raman Scattering Microscopy

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Published on: July 25, 2022

Raw Spectral Filter Array Imaging for Scene Recognition.

Hassan Askary¹, Jon Yngve Hardeberg^1,2, Jean-Baptiste Thomas^1,2,3

¹Department of Computer Science, NTNU-Norwegian University of Science and Technology, 2815 Gjøvik, Norway.

Sensors (Basel, Switzerland)

|March 28, 2024

Summary

This summary is machine-generated.

This study introduces scene recognition using raw multispectral images from spectral filter array cameras, bypassing demosaicing for improved accuracy. Higher resolution raw images enhance performance, even with mosaic patterns.

Keywords:

convolutional neural networks scene recognition spectral filter array

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Area of Science:

Computer Vision
Image Processing
Machine Learning

Background:

Scene recognition identifies image environments.
Spectral filter array cameras capture multispectral images quickly.
Current methods demosaic raw images, adding latency and artifacts.

Purpose of the Study:

To explore scene recognition directly on raw spectral filter array images.
To overcome limitations imposed by demosaicing artifacts and latency.
To develop a novel approach for multispectral image scene classification.

Main Methods:

Utilized convolutional neural networks (CNNs) for classification.
Developed a new raw image dataset for scene recognition.
Employed a pretrained Places-CNN model adapted for nine spectral channels.
Implemented a label mapping scheme for the new dataset.

Main Results:

Scene recognition performed directly on raw images shows promise.
Higher-resolution raw images yielded better classification performance.
The CNN model effectively utilized all nine spectral channels.
Pre-processing steps were evaluated for their impact on results.

Conclusions:

Directly processing raw spectral filter array images is a viable approach for scene recognition.
Bypassing demosaicing reduces latency and avoids associated artifacts.
The proposed method offers a more efficient and potentially accurate alternative for scene recognition tasks.