Fast centre–surround contrast modification

Vonikakis, Vassilios; Andreadis, Ioannis; Γαστεράτος, Αντώνιος

doi:10.1049/iet-ipr:20070012

Cited by 69 publications

(55 citation statements)

References 30 publications

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“…They can be grouped into two classes: conventional image enhancement algorithms and recovery algorithms based on the image degradation model. Conventional image enhancement algorithms, such as histogram specification [32], retinex [33] and contrast modification [34], have been widely used in computer vision and image processing. These algorithms work well when all the objects in an image have similar distances from the camera, such as an aerial image directly taken from up to down.…”

Section: Removing Weather Effectsmentioning

confidence: 99%

Image contrast enhancement for outdoor machine vision applications

Wahab

Zakaria

Latip

et al. 2013

2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR)

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Abstract-Outdoor machine vision is getting a concern nowadays. Ranging from surveillance and monitoring system to automotive system such as driver assistance system require vision application or artificial eye to keep monitoring the situations. However, most of these applications works very well during clear weather and degrade during bad weather due to the atmospheric particles mitigate the quality of vision system. This paper discuss the state of the art of image enhancement techniques used to adjust the contrast of an outdoor image degrade by fog, haze, and rain. A brief overview of bad weather will be discussed and several recent techniques on removing fog, haze and rain are discussed.

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Section: Removing Weather Effectsmentioning

confidence: 99%

Image contrast enhancement for outdoor machine vision applications

Wahab

Zakaria

Latip

et al. 2013

2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR)

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“…Both implementations do not meet the VGA standard (color images of 640×480 pixels size and 25fps) in video rendering. Recently, an alternative enhancement algorithm, inspired by the shunting characteristics of the center-surround cells of the Human Visual System (HVS), has been presented in [7]. It exhibits low complexity, as well as the fastest execution times, compared to the algorithms of the previous two implementations.…”

Section: Figmentioning

confidence: 99%

“…A detailed description of the algorithm is out of the scope of this chapter. Extensive details can be found in [7].…”

Section: Structure Of the Algorithmmentioning

confidence: 99%

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Real-Time Biologically-Inspired Image Exposure Correction

Vonikakis

Iakovidou

Andreadis

2010

IFIP Advances in Information and Communication Technology

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Abstract. This chapter presents a real-time FPGA implementation of a biologically-inspired image enhancement algorithm. The algorithm compensates for the under/over-exposed image regions, emerging when High Dynamic Range (HDR) scenes are captured by contemporary imaging devices. The transformations of the original algorithm, which are necessary in order to meet the requirements of an FPGA-based hardware system, are presented in detail. The proposed implementation, which is synthesized in Altera's Stratix II GX: EP2SGX130GF1508C5 FPGA device, features pipeline architecture, allowing the real-time rendering of color video sequences (25fps) with frame sizes up to 2.5Mpixels.

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“…Among the biological approaches [12] uses a centresurround receptive field model for contrast enhancement. However they uses only a single receptive field type namely the on-centre off-surround.…”

Section: Related Workmentioning

confidence: 99%

Biologically Inspired Contrast Enhancement Using Asymmetric Gain Control

Khwaja

Goecke

2009

2009 Digital Image Computing: Techniques and Applications

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A neuro-physiologically inspired model is presented for the contrast enhancement of images. The contrast of an image is calculated using simulated on-and off-centre receptive fields whereby obtaining the corresponding two contrast maps. We propose an adaptive asymmetric gain control function that is applied to the two contrast maps which are then used to reconstruct the image resulting in its contrast enhancement. The image's mean luminance can be adjusted as desired by adjusting the asymmetricity between the gain control factors of the two maps. The model performs local contrast enhancement in the contrast domain of an image where it lends itself very naturally to such adjustments. Furthermore, the model is extended on to colour images using the concept of colour-opponent receptive fields found in the human visual system. The colour model enhances the contrast right in the colour space without extracting the luminance information from it. Being neurophysiologically plausible, this model can be beneficial in theorising and understanding the gain control mechanisms in the primate visual system. We compare our results with the CLAHE algorithm.

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Fast centre–surround contrast modification

Cited by 69 publications

References 30 publications

Image contrast enhancement for outdoor machine vision applications

Image contrast enhancement for outdoor machine vision applications

Real-Time Biologically-Inspired Image Exposure Correction

Biologically Inspired Contrast Enhancement Using Asymmetric Gain Control

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