Visual signal detection in structured backgrounds. II. Effects of contrast gain control, background variations, and white noise

Miguel P. Eckstein; Albert J. Ahumada; Andrew B. Watson

doi:10.1364/JOSAA.14.002406

Journal of the Optical Society of America A
Vol. 14,
Issue 9,
pp. 2406-2419
(1997)
•https://doi.org/10.1364/JOSAA.14.002406

Visual signal detection in structured backgrounds. II. Effects of contrast gain control, background variations, and white noise

Miguel P. Eckstein, Albert J. Ahumada, and Andrew B. Watson

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Miguel P. Eckstein, Albert J. Ahumada, and Andrew B. Watson, "Visual signal detection in structured backgrounds. II. Effects of contrast gain control, background variations, and white noise," J. Opt. Soc. Am. A 14, 2406-2419 (1997)

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Abstract

Studies of visual detection of a signal superimposed on one of two identical backgrounds show performance degradation when the background has high contrast and is similar in spatial frequency and/or orientation to the signal. To account for this finding, models include a contrast gain control mechanism that pools activity across spatial frequency, orientation and space to inhibit (divisively) the response of the receptor sensitive to the signal. In tasks in which the observer has to detect a known signal added to one of $M$ different backgrounds due to added visual noise, the main sources of degradation are the stochastic noise in the image and the suboptimal visual processing. We investigate how these two sources of degradation (contrast gain control and variations in the background) interact in a task in which the signal is embedded in one of $M$ locations in a complex spatially varying background (structured background). We use backgrounds extracted from patient digital medical images. To isolate effects of the fixed deterministic background (the contrast gain control) from the effects of the background variations, we conduct detection experiments with three different background conditions: (1) uniform background, (2) a repeated sample of structured background, and (3) different samples of structured background. Results show that human visual detection degrades from the uniform background condition to the repeated background condition and degrades even further in the different backgrounds condition. These results suggest that both the contrast gain control mechanism and the background random variations degrade human performance in detection of a signal in a complex, spatially varying background. A filter model and added white noise are used to generate estimates of sampling efficiencies, an equivalent internal noise, an equivalent contrast-gain-control-induced noise, and an equivalent noise due to the variations in the structured background.

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	Noise rms
	0	0.071	0.125	0.18	0.25
Observer KS
Uniform background	$δ = 17.6$	$δ = 7.1$	$δ = 3.8$	$δ = 2.6$	$δ = 1.95$
	$U = 0$	$U = 2$	$U = 1$	$U = 0$	$U = 0$
Same structured background	$δ = 7.0$	$δ = 3.9$	$δ = 2.8$	$δ = 2.23$	$δ = 1.92$
	$U = 0$	$U = 0$	$U = 0$	$U = 0$	$U = 1$
Different structured background	$δ = 4.2$	$δ = 3.4$	$δ = 2.7$	$δ = 2.2$	$δ = 1.94$
	$U = 0$	$U = 0$	$U = 1$	$U = 1$	$U = 2$
Observer GN
Uniform background	$δ = 7.2$	$δ = 6.5$	$δ = 3.3$	$δ = 2.4$	$δ = 1.83$
	$U = 0$	$U = 1$	$U = 1$	$U = 0$	$U = 0$
Same structured background	$δ = 4.7$	$δ = 4.3$	$δ = 2.1$	$δ = 1.89$	$δ = 1.81$
	$U = 0$	$U = 1$	$U = 0$	$U = 0$	$U = 1$
Different structured background	$δ = 3.31$	$δ = 2.7$	$δ = 2.0$	$δ = 1.9$	$δ = 1.79$
	$U = 0$	$U = 0$	$U = 0$	$U = 1$	$U = 2$

Parameter	Best Fit	Constraint	$df$	$χ^{2} / df$
Sampling efficiency (uniform, $J_{ub}$ )	0.316	No constraints	54	3.67
Sampling efficiency (same structured background, $J_{sb}$ )	0.166	$J_{ub} = J_{sb} = J_{rb}$	2	$- 1.65$ ^b
Sampling efficiency (different structured background, $J_{rb}$ )	0.166	$J_{sb} = J_{rb}$	1	$- 4.03$ ^b
Equivalent internal noise ^c	${σ_{i}}^{2} = {0.032}^{2}$
Equivalent contrast gain control noise	${σ_{cgc}}^{2} = {0.049}^{2}$	${σ_{cgc}}^{2} = 0$	1	178.7
Equivalent background variation noise	${σ_{rbv}}^{2} = {0.075}^{2}$	${σ_{rbv}}^{2} = 0$	1	248.8

Parameter	Best Fit	Constraint	$df$	$χ^{2} / df$
Sampling efficiency (uniform, $J_{ub}$ )	0.308	No constraints	54	5.2
Sampling efficiency (same structured background, $J_{sb}$ )	0.166	$J_{ub} = J_{sb} = J_{rb}$	2	9.0
Sampling efficiency (different structured background, $J_{rb}$ )	0.2083	$J_{sb} = J_{rb}$	1	18.00
Equivalent internal noise ^a	${σ_{i}}^{2} = {0.074}^{2}$
Equivalent contrast gain control noise	${σ_{cgc}}^{2} = {0.04}^{2}$	${σ_{cgc}}^{2} = 0$	1	27.2
Equivalent background variation noise	${σ_{rbv}}^{2} = {0.12}^{2}$	${σ_{rbv}}^{2} = 0$	1	56.07

	Noise rms
	0	0.071	0.125	0.18	0.25
Observer KS
Uniform background	$δ = 17.6$	$δ = 7.1$	$δ = 3.8$	$δ = 2.6$	$δ = 1.95$
	$U = 0$	$U = 2$	$U = 1$	$U = 0$	$U = 0$
Same structured background	$δ = 7.0$	$δ = 3.9$	$δ = 2.8$	$δ = 2.23$	$δ = 1.92$
	$U = 0$	$U = 0$	$U = 0$	$U = 0$	$U = 1$
Different structured background	$δ = 4.2$	$δ = 3.4$	$δ = 2.7$	$δ = 2.2$	$δ = 1.94$
	$U = 0$	$U = 0$	$U = 1$	$U = 1$	$U = 2$
Observer GN
Uniform background	$δ = 7.2$	$δ = 6.5$	$δ = 3.3$	$δ = 2.4$	$δ = 1.83$
	$U = 0$	$U = 1$	$U = 1$	$U = 0$	$U = 0$
Same structured background	$δ = 4.7$	$δ = 4.3$	$δ = 2.1$	$δ = 1.89$	$δ = 1.81$
	$U = 0$	$U = 1$	$U = 0$	$U = 0$	$U = 1$
Different structured background	$δ = 3.31$	$δ = 2.7$	$δ = 2.0$	$δ = 1.9$	$δ = 1.79$
	$U = 0$	$U = 0$	$U = 0$	$U = 1$	$U = 2$

Parameter	Best Fit	Constraint	$df$	$χ^{2} / df$
Sampling efficiency (uniform, $J_{ub}$ )	0.316	No constraints	54	3.67
Sampling efficiency (same structured background, $J_{sb}$ )	0.166	$J_{ub} = J_{sb} = J_{rb}$	2	$- 1.65$ ^b
Sampling efficiency (different structured background, $J_{rb}$ )	0.166	$J_{sb} = J_{rb}$	1	$- 4.03$ ^b
Equivalent internal noise ^c	${σ_{i}}^{2} = {0.032}^{2}$
Equivalent contrast gain control noise	${σ_{cgc}}^{2} = {0.049}^{2}$	${σ_{cgc}}^{2} = 0$	1	178.7
Equivalent background variation noise	${σ_{rbv}}^{2} = {0.075}^{2}$	${σ_{rbv}}^{2} = 0$	1	248.8

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Journal of the Optical Society of America A