Browsing by Author "Vincent Mary School of Engineering"
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    An Alternative Single-Image Super Resolution Framework Employing High Frequency Prediction Using A Robust Huber Rational Function
    ( 2015-11) Kornkamol Thakulsukannant ; Vorapoj Patanavijit ; Vincent Mary School of Engineering
    In general prospective, SI-SR or Single-Image Super-Resolution, which is one of the most useful algorithms of Super Resolution-Reconstruction (SRR) algorithms, is a mathematical procedure for acquiring a high-resolution image from only one coarse-resolution image, which is usually computed by Digital Image Processing (DIP). Even thought there have been substantially researched during the last decade, Single - Image Super-Resolution for applying on real implementations still keeps throw down the gauntlet. One of the practical Single- Image Super-Resolution is the resolution enhancement using prediction of the high-frequency image because of its high performance and its less comple xity however the rational function C(x, y) of high-frequency image prediction process of this technique is depend upon three parameters (b, h, k) therefore the parameter turning is difficult for maximizing its performance. From this problem prospective, this paper presents the alternative SI-SR framework employing robust rational function based on Huber function, which is depend upon only one parameter (T), instead of three parameters like the rational function C(x,y). Using up to fourteen standard images, which are crooked by varied noise models, in analysis testing section, the proposed SI-SR is demonstrated to be somewhat simper than the original SI-SR with equivalent efficiency because the saving in parameter turning time will be very important for SI-SR in real implementations.
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    Experimental Study on Image Reconstruction from Spatial Correlation-based Optical Flow Motion Vector over Non Gaussian Noise Contamination using Reversed Confidential with Bilateral Filter
    ( 2016) Darun Kesrarat ; Vorapoj Patanavijit ; Vincent Mary School of Engineering
    In motion estimation, noise is a verity to degrade the performance in optical flow for determining motion vector. This paper examines the performance of noise tolerance model in spatial correlation-based optical flow for image reconstruction from motion vector where the source sequences are contaminated by non Gaussian noise. There are Poisson Noise, Salt & Pepper Noise, and Speckle Noise. In the experiment, several standard sequences in different styles are used and the applied combination model of reversed confidential with bilateral filter on spatial correlation-based optical flow is mainly focused to determined the best condition to apply this model with. The result in image reconstruction from motion vector is used in performance comparison with traditional noise tolerance models by using Peak Signal to Noise Ratio (PSNR) as a primary index for studying.
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    Robust Block-Based Motion Estimation for Image Reconstruction Using Bi-direction Confidential
    ( 2014) Darun Kesrarat ; Vorapoj Patanavijit ; Vincent Mary School of Engineering
    In block-based motion estimation where the outcome of the motion vector (MV) is used to reconstruct the image, noise is one of the major problems that impact the quality of the performance in image reconstruction. There are several aspects to improve the quality of the reconstructed image but we focus on improvement of the accuracy in MV from existing block-based motion estimation algorithms when they applies our proposed model only without other any additional models. Because we would like to prove that our proposed model improves an accuracy of the MV that it leads to the better quality of the reconstructed image as a result. This paper presents robust block-based motion estimation where bidirection confidential model is applied over the existing blockbased motion estimation algorithm to improve the accuracy of the MV itself. In the experiment where we simulated several Additive White Gaussian Noise (AWGN) levels over several experiment sequences, we found that the proposed model improved the quality of the reconstructed image when it is applied over several existing block-based motion estimation algorithms. In our experiment, we evaluated the quality of reconstructed image by using Peak Signal to Noise Ratio (PSNR).