Journal ArticleDOI
Contrast in complex images.
TLDR
A definition of local band-limited contrast in images is proposed that assigns a contrast value to every point in the image as a function of the spatial frequency band and is helpful in understanding the effects of image-processing algorithms on the perceived contrast.Abstract:
The physical contrast of simple images such as sinusoidal gratings or a single patch of light on a uniform background is well defined and agrees with the perceived contrast, but this is not so for complex images. Most definitions assign a single contrast value to the whole image, but perceived contrast may vary greatly across the image. Human contrast sensitivity is a function of spatial frequency; therefore the spatial frequency content of an image should be considered in the definition of contrast. In this paper a definition of local band-limited contrast in images is proposed that assigns a contrast value to every point in the image as a function of the spatial frequency band. For each frequency band, the contrast is defined as the ratio of the bandpass-filtered image at the frequency to the low-pass image filtered to an octave below the same frequency (local luminance mean). This definition raises important implications regarding the perception of contrast in complex images and is helpful in understanding the effects of image-processing algorithms on the perceived contrast. A pyramidal image-contrast structure based on this definition is useful in simulating nonlinear, threshold characteristics of spatial vision in both normal observers and the visually impaired.read more
Citations
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Journal ArticleDOI
Image contrast enhancement via entropy production
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Journal ArticleDOI
Generalized Talbot self-healing and noise mitigation of faulty periodic images
Luis Romero Cortes,Hugues Guillet de Chatellus,Antonin Deville,Mohamed Seghilani,Ines Hamam,José Azaña +5 more
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Proceedings ArticleDOI
A color image quality assessment using a reduced-reference image machine learning expert
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Pansharpening with a Gradient Domain GIF Based on NSST
Jiao Jiao,Lingda Wu +1 more
TL;DR: The proposed pansharpening method with a gradient domain guided image filter (GIF) that is based on non-subsampled shearlet transform (NSST) is superior to the state-of-the-art panshARPening methods, and it can efficiently improve the spatial quality and spectral maintenance.
Journal ArticleDOI
The face inversion effect in opponent-stimulus rivalry
TL;DR: This work studied continuous rivalry across more than 600 dominance epochs for each observer, having faces and houses rival against their inverted counterparts, and letting faces rival against houses in both upright and inverted orientation.
References
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Journal ArticleDOI
Chapter 9 Visual adaptation and retinal gain controls
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Journal ArticleDOI
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Mark A. Georgeson,G.D. Sullivan +1 more
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