Enhancement of image luminance resolution by imposing random jitter

Yi, Daqing; Jiang, Ping; Mallen, Edward A. H.; Wang, Xiaonian; Zhu, Jinkang

doi:10.1007/s00521-010-0433-1

Cited by 3 publications

(2 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Prokopowicz and Cooper ( 1993 ) proposed DR as a technique that employs perturbations of mobile robots to enhance spatial data processing. Image sensors perturbed by vibrations or noise have been used for spatial enhancement through edge, luminance and contrast detection (Landolt et al, 2001 ; Greschner et al, 2002 ; Hennig et al, 2002 ; Donner and Hemilä, 2007 ; Rucci et al, 2007 ; Yi et al, 2011 ).…”

Section: Introductionmentioning

confidence: 99%

A Saccade Based Framework for Real-Time Motion Segmentation Using Event Based Vision Sensors

et al. 2017

View full text Add to dashboard Cite

Motion segmentation is a critical pre-processing step for autonomous robotic systems to facilitate tracking of moving objects in cluttered environments. Event based sensors are low power analog devices that represent a scene by means of asynchronous information updates of only the dynamic details at high temporal resolution and, hence, require significantly less calculations. However, motion segmentation using spatiotemporal data is a challenging task due to data asynchrony. Prior approaches for object tracking using neuromorphic sensors perform well while the sensor is static or a known model of the object to be followed is available. To address these limitations, in this paper we develop a technique for generalized motion segmentation based on spatial statistics across time frames. First, we create micromotion on the platform to facilitate the separation of static and dynamic elements of a scene, inspired by human saccadic eye movements. Second, we introduce the concept of spike-groups as a methodology to partition spatio-temporal event groups, which facilitates computation of scene statistics and characterize objects in it. Experimental results show that our algorithm is able to classify dynamic objects with a moving camera with maximum accuracy of 92%.

show abstract

Section: Introductionmentioning

confidence: 99%

A Saccade Based Framework for Real-Time Motion Segmentation Using Event Based Vision Sensors

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Some minimum blur does occur due to optical limitations and due to the minimum size o f the photoreceptors, and it would be perceived w ithin static images. However with fixational eye movements (such as jitter) over a static scene and along with spatiotemporal integration, such static blur could in principle be reduced (e.g., Yi, Jiang, & Mallen, 2011). It is not clear w hether hum an cognition takes advantage o f this de-jitter capability.…”

Section: Motion Blur Is Physical and Unavoidablementioning

confidence: 99%

A unified cognitive model of visual filling-in based on an Emergic Network architecture

Leibovitz¹

View full text Add to dashboard Cite

The author has granted a non exclusive license allowing Library and Archives Canada to reproduce, publish, archive, preserve, conserve, communicate to the public by telecommunication or on the Internet, loan, distrbute and sell theses worldwide, for commercial or non commercial purposes, in microform, paper, electronic and/or any other formats. AVIS:L'auteur a accorde une licence non exclusive permettant a la Bibliotheque et Archives Canada de reproduire, publier, archiver, sauvegarder, conserver, transmettre au public par telecommunication ou par I'lnternet, preter, distribuer et vendre des theses partout dans le monde, a des fins commerciales ou autres, sur support microforme, papier, electronique et/ou autres formats.Bien que ces formulaires aient inclus dans la pagination, il n'y aura aucun contenu manquant.

show abstract

A bio-inspired synergistic virtual retina model for tone mapping

Benzi

Escobar

Kornprobst³

2018

Computer Vision and Image Understanding

View full text Add to dashboard Cite

Real-world radiance values span several orders of magnitudes which have to be processed by artificial systems in order to capture visual scenes with a high visual sensitivity. Interestingly, it has been found that similar processing happens in biological systems, starting at the retina level. So our motivation in this paper is to develop a new video tone mapping operator (TMO) based on a synergistic model of the retina. We start from the so-called Virtual Retina model, which has been developed in computational neuroscience. We show how to enrich this model with new features to use it as a TMO, such as color management, luminance adaptation at photoreceptor level and readout from a heterogeneous population activity. Our method works for video but can also be applied to static images (by repeating images in time). It has been carefully evaluated on standard benchmarks in the static case, giving comparable results to the state-of-theart using default parameters, while offering user control for finer tuning. Results on HDR videos are also promising, specifically w.r.t. temporal luminance coherency. As a whole, this paper shows a promising way to address computational photography challenges by exploiting the current research in neuroscience about retina processing.Real-world radiance values span several orders of magnitudes which have to be processed by artificial 2 systems in order to capture visual scenes with a high visual sensitivity. Think about scenes of twilight, day 3 sunlight, the stars at night or the interiors of a house. To capture these scenes, one needs cameras capable 4 of capturing so-called high dynamic range (HDR) images, which are expensive, or via the method proposed 5 by [21], currently implemented in most standard cameras. The problem is how to visualize these images 6 afterwards since standard monitors have a low dynamic range (LDR). Two kinds of solutions exist. The 7 first is technical: there are HDR displays, but they are not affordable for the general public yet. The second 8 is algorithmic: there are methods to compress the range of intensities from HDR to LDR. These methods 9 are called tone mapping operators (TMOs) [84]. TMOs have been developed for both static scenes and 10 videos rather independently. There has been intensive work on static images (see [49, 11] for reviews), with 11 approaches combining luminance adaptation and local contrast enhancement sometimes closely inspired 12 from retinal principles, as in [64, 8, 30, 67] just to cite a few. Recent developments concern video-tone 13 mapping, where a few approaches have been developed so far (see [28, 27] for surveys). 14 Interestingly, in neuroscience it has been found that a similar tone mapping processing occurs in the retina 15 through adaptation mechanisms. This is crucial since the retina must maintain high contrast sensitivity 16 over this very broad range of luminance in natural scenes [86, 34,44]. Adaptation is both global through 17 neuromodulatory feedback loops and local through adaptive gain control mechanisms so ...

show abstract

Enhancement of image luminance resolution by imposing random jitter

Cited by 3 publications

References 25 publications

A Saccade Based Framework for Real-Time Motion Segmentation Using Event Based Vision Sensors

A Saccade Based Framework for Real-Time Motion Segmentation Using Event Based Vision Sensors

A unified cognitive model of visual filling-in based on an Emergic Network architecture

A bio-inspired synergistic virtual retina model for tone mapping

Contact Info

Product

Resources

About