End-to-end optimization of prosthetic vision

Jaap de Ruyter van Steveninck; Umut Güçlü; Richard van Wezel; Marcel van Gerven

doi:10.1167/jov.22.2.20

PMC full text:

J Vis. 2022 Feb; 22(2): 20.

Published online 2022 Feb 28. doi: 10.1167/jov.22.2.20

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Table 3.

Performance metrics for Experiment 3.

	Intensity reconstruction			Perceptual reconstruction			Semantic boundary prediction
Processing model	MSE	SSIM	FSIM	MSE	SSIM	FSIM	Acc.	Sens.	Spec.	Prec.	AUC
End-to-end (ours)	0.034	0.554	0.719	0.064	0.541	0.761	0.697	0.722	0.695	0.165	0.785
Canny	0.055	0.443	0.672	0.061	0.454	0.581	0.598	0.774	0.583	0.134	0.746
HED	0.056	0.454	0.708	0.059	0.458	0.588	0.757	0.571	0.772	0.173	0.724

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MSE, mean squared error; FSIM, feature similarity index; SSIM, structural similarity index; Acc., accuracy, defined as the proportion of correctly classified pixels; Sens., sensitivity, defined as the proportion of boundary pixels that were correctly identified as such; Spec., specificity, defined as the proportion of non-boundary pixels that were correctly identified as such. AUC, area under the receiver-operator curve.

Significant highest performances are indicated in bold.