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Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy

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Published on: December 9, 2013

Color image splicing localization algorithm by quaternion fully convolutional networks and superpixel-enhanced

Bei Jing Chen^1,2,3, Ye Gao¹, Ling Zheng Xu⁴

¹Jiangsu Engineering Center of Network Monitoring, School of Computer < Software, Nanjing University of Information Science < Technology, Nanjing 210044, China.

Mathematical Biosciences and Engineering : MBE

|November 9, 2019

Summary

This study introduces a novel quaternion fully convolutional network (QFCN) for accurate color image splicing localization. The new method effectively captures inter-channel correlations, outperforming existing deep learning and conventional approaches.

Keywords:

conditional random field fully convolutional network quaternion splicing detection splicing localization

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Area of Science:

Computer Vision
Image Processing
Deep Learning

Background:

Existing fully convolutional network (FCN) algorithms for image splicing localization process color channels independently.
This separation leads to a failure in capturing the inherent correlations between color channels, impacting localization accuracy.

Purpose of the Study:

To propose a novel quaternion fully convolutional network (QFCN) to generalize FCN into the quaternion domain.
To develop an improved color image splicing localization algorithm by integrating QFCNs with superpixel (SP)-enhanced pairwise conditional random field (CRF).

Main Methods:

The proposed method replaces real-valued blocks in conventional FCNs with quaternion conventional blocks to form QFCNs.
Three QFCN versions (QFCN32, QFCN16, QFCN8) with varying up-sampling layers were explored.
A superpixel (SP)-enhanced pairwise conditional random field (CRF) was employed for refining QFCN output.

Main Results:

Experimental results on three public datasets show superior performance compared to existing methods.
The QFCN-based algorithm demonstrated enhanced accuracy in localizing spliced regions.
The integration with SP-enhanced pairwise CRF effectively refined the localization results.

Conclusions:

The proposed QFCN-based algorithm offers a significant advancement in color image splicing localization.
The quaternion-based approach effectively addresses the limitations of real-valued FCNs in handling color channel correlations.
The method provides state-of-the-art performance for image splicing detection.