Topic
Contrast (vision)
About: Contrast (vision) is a research topic. Over the lifetime, 10379 publications have been published within this topic receiving 221480 citations.
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TL;DR: The results suggest that alterations occur already at the retinal level where dopamine receptors have been found, and the reported changes of the VEP are not caused by the visual cortex alone.
123 citations
TL;DR: A neural network model of brightness perception is developed to account for a wide variety of data, including the classical phenomenon of Mach bands, low- and high-contrast missing fundamental, luminance staircases, and non-linear contrast effects associated with sinusoidal waveforms.
123 citations
TL;DR: The results show that attention and adaptation affect the contrast psychometric function in a similar but opposite way: Attention increases stimulus salience, whereas adaptation reduces stimulus Salience.
Abstract: Attention and adaptation are both mechanisms that optimize visual performance. Attention optimizes performance by increasing contrast sensitivity for and neural response to attended stimuli while decreasing them for unattended stimuli; adaptation optimizes performance by increasing contrast sensitivity for and neural response to changing stimuli while decreasing them for unchanging stimuli. We investigated whether and how the adaptation state and the attentional effect on contrast sensitivity interact. We measured contrast sensitivity with an orientation-discrimination task, in two adaptation conditions--adapt to 0% or 100% contrast--in focused, distributed, and withdrawn attentional conditions. We used threshold and asymptotic performance to index the magnitude of the attentional effect--enhancement or impairment in contrast sensitivity--before and after adapting to high-contrast stimuli. The results show that attention and adaptation affect the contrast psychometric function in a similar but opposite way: Attention increases stimulus salience, whereas adaptation reduces stimulus salience. An interesting finding is that the adaptation state does not modulate the magnitude of the attentional effect. This suggests that attention affects the normalized signal once the effect of contrast adaptation has taken place and that these two mechanisms act separately to change contrast sensitivity. Attention can overcome adaptation to restore contrast sensitivity.
123 citations
TL;DR: Considering both pattern complexity and luminance contrast, a novel spatial masking estimation function is deduced, and an improved JND estimation model is built, which performs highly consistent with the human perception.
Abstract: The just noticeable difference (JND) in an image, which reveals the visibility limitation of the human visual system (HVS), is widely used for visual redundancy estimation in signal processing. To determine the JND threshold with the current schemes, the spatial masking effect is estimated as the contrast masking, and this cannot accurately account for the complicated interaction among visual contents. Research on cognitive science indicates that the HVS is highly adapted to extract the repeated patterns for visual content representation. Inspired by this, we formulate the pattern complexity as another factor to determine the total masking effect: the interaction is relatively straightforward with a limited masking effect in a regular pattern, and is complicated with a strong masking effect in an irregular pattern. From the orientation selectivity mechanism in the primary visual cortex, the response of each local receptive field can be considered as a pattern; therefore, in this paper, the orientation that each pixel presents is regarded as the fundamental element of a pattern, and the pattern complexity is calculated as the diversity of the orientation in a local region. Finally, considering both pattern complexity and luminance contrast, a novel spatial masking estimation function is deduced, and an improved JND estimation model is built. Experimental results on comparing with the latest JND models demonstrate the effectiveness of the proposed model, which performs highly consistent with the human perception. The source code of the proposed model is publicly available at http://web.xidian.edu.cn/wjj/en/index.html.
123 citations
01 Dec 2004
TL;DR: A fast approach for image contrast enhancement, based on localized contrast manipulation, which is not only last and easy to implement, but also has several other promising properties (adaptive, multiscale, weighted localization, etc.).
Abstract: In this paper we describe a fast approach for image contrast enhancement, based on localized contrast manipulation. Our approach is not only last and easy to implement, but also has several other promising properties (adaptive, multiscale, weighted localization, etc.). We will also discuss in this paper an anisotropic version of our approach. Several examples of medical images, including brain MR images, chest CT images and mammography images, will be provided to demonstrate the performance of our approach.
123 citations