Entropic contrast enhancement

Khellaf, A.; Beghdadi, Azeddine; Dupoisot, H.

doi:10.1109/42.108593

Cited by 48 publications

(19 citation statements)

References 16 publications

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“…There are some implementing problems using local information measure from work [11]. A.Beghdadi and A.Khellaf [12] worked with (4) as a criterion to decide a pixel is corrupted or not.…”

Section: B Problems With Local Information Measurementioning

confidence: 99%

Restoration of Highly Impulses Corrupted Color Image by Local Entropy Measure

You

2014

2014 International Symposium on Computer, Consumer and Control

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This paper proposes an effective scheme using local entropy measure to restore highly salt-pepper impulses up to 99% corrupted colour image. A local information measure to restore impulses corrupted image had been proposed before. Its restoration quality is not satisfactory at high impulse densities. The proposed algorithm presents significant quality of restored image than above proposed method not only for grey images but also for colour images. In addition, its preservation of edges and details is superior at high impulse densities, also, this scheme presents good empirical results by both the visual quality and quantitative performances in terms of peak signal to noise ratio (PSNR) and normalized colour difference (NCD) .

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“…There are some implementing problems using local information measure from work [11]. A.Beghdadi and A.Khellaf [12] worked with (4) as a criterion to decide a pixel is corrupted or not.…”

Section: B Problems With Local Information Measurementioning

confidence: 99%

Restoration of Highly Impulses Corrupted Color Image by Local Entropy Measure

You

2014

2014 International Symposium on Computer, Consumer and Control

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“…Since the pioneer work of Frieden [9], the use of entropy [10], [11] in image analysis has attracted a great number of researchers, especially in image reconstruction [12], [13] and segmentation [14], [15]. In our previous work [16] that followed the idea of Shiozaki [17], which consists in defining a local entropy and thus performing a local treatment, we have shown that by amplifying the local entropy of the contrast, one can get a contrasted image. It has also been suggested that a noisefiltering treatment can be obtained by decreasing the entropy of the local contrast in a given neighborhood.…”

Section: Introductionmentioning

confidence: 97%

A noise-filtering method using a local information measure

Beghdadi

Khellaf²

1997

IEEE Trans. on Image Process.

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A nonlinear-noise filtering method for image processing, based on the entropy concept is developed and compared to the well-known median filter and to the center weighted median filter (CWM). The performance of the proposed method is evaluated through subjective and objective criteria. It is shown that this method performs better than the classical median for different types of noise and can perform better than the CWM filter in some cases.

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“…Contrast enhancement is an important area especially in image recognition and related fields. Traditionally, contrast enhancement has been realized by a proper selection of wavelet filters, enhancement functions (Fan and Laine, 1996), multimodal histograms (Grundland and Dodgson, 2006), entropy operators (Khellaf et al, 1991), to name a few.…”

Section: Introductionmentioning

confidence: 99%

Cost effective localization in distributed sensory networks

Coşkun

Sevil

Ozdemir

2011

Engineering Applications of Artificial Intelligence

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a b s t r a c tThe most important mechanism to occur in biological distributed sensory networks (DSNs) is called lateral inhibition, (LI). LI relies on one simple principle. Each sensor strives to suppress its neighbors in proportion to its own excitation. In this study, LI mechanism is exploited to localize the unknown position of a light source that illuminated the photosensitive sensory network containing high and low quality sensors. Each photosensitive sensor was then calibrated to accurately read the distance to the light source. A series of experiments were conducted employing both quality sensors. Low quality array was allowed to take advantage of LI, whereas the high quality one was not. Results showed that the lateral inhibition mechanism increased the sensitivity of inferior quality sensors, giving the ability to make the localization as sensitive as high quality sensors do. This suggests that the networks with multitude of sensors could be made cost-effective, were these sensory networks equipped with LI.

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Entropic contrast enhancement

Cited by 48 publications

References 16 publications

Restoration of Highly Impulses Corrupted Color Image by Local Entropy Measure

Restoration of Highly Impulses Corrupted Color Image by Local Entropy Measure

A noise-filtering method using a local information measure

Cost effective localization in distributed sensory networks

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