Independence of luminance and contrast in natural scenes and in the early visual system

By Valerio Mante, Robert A. Frazor, Vincent Bonin, Wilson S. Geisler, Matteo Carandini. 2005

393 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

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Citations in the corpus

Normalization as a canonical neural computation (2011, background): “Within a given visual scene the variation is typically smaller, but still more than a factor of 10 (Ref”
Cellular Basis for Contrast Gain Control over the Receptive Field Center of Mammalian Retinal Ganglion Cells (2007, background): “Ganglion cells adapt to the mean luminance through “light adaptation,” but also to the dynamic range of luminance through “contrast adaptation” (Walraven et al., 1990; Troy and Enroth-Cugell, 1993; Demb, 2002; Baccus and Meister, 2004; Mante et al., 2005).”
The Analysis of Image Contrast: From Quality Assessment to Automatic Enhancement (2016, background): “the mean luminance of the input image to be a proper value, especially for natural images [47].”
Visual adaptation: neural, psychological and computational aspects. (2007, background): “Recent research has shown that neuronal responses to naturalistic stimuli cannot necessarily be inferred from the responses to conventional ‘laboratory’ stimuli such as sine wave gratings (Mante, Frazor, Bonin, Geisler, & Carandini, 2005; Shar-pee et al., 2006).”
The Normalization Model of Attention (2009, result): “Normalization of visual cortical responses is analogous to earlier models of retinal light adaptation (Sperling and Sondhi, 1968) and to models of contrast gain control in the retina and LGN (Baccus and Meister, 2002; Bonin et al., 2005; Kaplan et al., 1987; Mante et al., 2005; Shapley and Victor, 1”
Visual perception and the statistical properties of natural scenes. (2008, background): “Similarly, Mante et al. (2005) measure responses of individual neurons in the LGN and found responses to be consistent with separable luminance and contrast gain control mechanisms.”
Do We Know What the Early Visual System Does? (2005, background): “…is completed within a cycle of the drifting grating, demonstrating that the gain control mechanisms operate in 100 ms (Enroth-Cugell and Shapley, 1973a; Saito and Fukada, 1986; Victor, 1987; Lankheet et al., 1993a; Yeh et al., 1996; Baccus and Meister, 2002; Lee et al., 2003; Mante et al., 2005).”
The Suppressive Field of Neurons in Lateral Geniculate Nucleus (2005, background): “In particular, we cannot distinguish the fast mechanism of contrast gain control (Victor, 1987; Baccus and Meister, 2002; Mante et al., 2005; Zaghloul et al., 2005) from the slower mechanism of contrast adaptation (Chander and Chichilnisky, 2001; Kim and Rieke, 2001; Baccus and Meister, 2002;…”
The Statistical Computation Underlying Contrast Gain Control (2006, neutral): “These previous results clarify the spatial extent of contrast gain control (Bonin et al., 2005) and the effect of contrast gain control on response amplitude (Bonin et al., 2005) and time course (Mante et al., 2005).”
Contrast Gain Control in Auditory Cortex (2011, neutral): “, 2003), but different from the visual system, where contrast has a considerable effect on the temporal dynamics of neural responses (Mante et al., 2005).”
The challenges natural images pose for visual adaptation. (2009, neutral): “Adapted with permission from Mante et al. (2005).”
Functional mechanisms shaping lateral geniculate responses to artificial and natural stimuli. (2008, methodology): “…contrast diminish both the amplitude of the RF, the neuron's gain, and the duration of its temporal profile, the neuron's integration time (Bonin et al., 2005; Demb, 2002; Kaplan and Benardete, 2001; Lesica et al., 2007; Mante et al., 2005; Meister and Berry, 1999; Shapley and Enroth-Cugell, 1984).”
Objective Quality Assessment of Tone-Mapped Images (2013, methodology): “We found that these histograms can be well ﬁtted using a Gaussian and a Beta probability density functions given by and Recentstudies suggested that brightness and contrast are largely independent quantities in terms of both natural image statistics and biological computation [32].”