Enlargement of Digital Images by Using Neural Network

Ren, Dexin; Zhao, Jun; Zhao, Jingjing; Wang, Shunhong

doi:10.1109/isda.2006.140

Cited by 1 publication

(1 citation statement)

References 2 publications

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“…Unlike orthogonal projections, this method uses simpler algorithms for image enlargement or reduction [6]. Image enlargement using fractals, neural networks, cellular neural networks, fuzzy logic, principal component analysis and other algorithms are abundant in the literature [7][8][9][10][11][12].…”

Section: Background On Pixel Duplicationmentioning

confidence: 99%

Analysis of filtering techniques and image quality in pixel duplicated images

Mehrübeoğlu

McLauchlan

2009

Mathematics for Signal and Information Processing

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When images undergo filtering operations, valuable information can be lost besides the intended noise or frequencies due to averaging of neighboring pixels. When the image is enlarged by duplicating pixels, such filtering effects can be reduced and more information retained, which could be critical when analyzing image content automatically. Analysis of retinal images could reveal many diseases at early stage as long as minor changes that depart from a normal retinal scan can be identified and enhanced. In this paper, typical filtering techniques are applied to an early stage diabetic retinopathy image which has undergone digital pixel duplication. The same techniques are applied to the original images for comparison. The effects of filtering are then demonstrated for both pixel duplicated and original images to show the information retention capability of pixel duplication. Image quality is computed based on published metrics. Our analysis shows that pixel duplication is effective in retaining information on smoothing operations such as mean filtering in the spatial domain, as well as lowpass and highpass filtering in the frequency domain, based on the filter window size. Blocking effects due to image compression and pixel duplication become apparent in frequency analysis.

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