Now showing items 1-19 of 19

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    An Alternative Technique using Median Filter for Image Reconstruction based on Partition Weighted Sum Filter 

    Vorapoj Patanavijit (2016-06)

    In this paper, we propose an alternative technique for image reconstruction which it is combined existing methods for better performance in spatial domain using the median (MED) filter based on partition weighted sum (PWS) filter. Four noise models are considered including additive white Gaussian noise (AWGN), poission noise (PN), salt and pepper noise (SPN) and speckle noise (SN) under different image types such as aerial image, face image, scenic image, and text image. The simulation results show that the median based partition weighted ...
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    Bidirectional Confidential with Bilateral Filter on Local Based Optical Flow for Image Reconstruction under Noisy Condition 

    Darun Kesrarat; Vorapoj Patanavijit (2015)

    More than a decade, Optical flow is relevant in many areas such as video coding and compression, robot vision, object tracking and segmentation, and super resolution reconstruction. By the result of the motion vector from optical flow, reduction the error stands a problem especially under noisy condition. Many models have been proposed to reduce the error and bilateral is one of the popular models. This paper introduces the model of bilateral filter in combination with bidirectional confidential over simple local based optical flow where ...
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    Empirical Exploration Achievement of Noise Removal Algorithm Based on Trilateral Filter for Both Gaussian and Impulsive Noise Ambiance 

    Vorapoj Patanavijit (2016-06)

    Although the Bilateral filter is one of the most realistic and virtuoso noise removal algorithms, which is often proposed for Gaussian noise in 1998, the Bilateral filter (BF) ineffectively works under the impulsive noise. Consequently, Trilateral filter (which is a modification Bilateral filter) was first proposed by Roman Garnett et al. in 2005 and this filter is based on the hybrid consisting of Bilateral filter and Rank-Ordered Absolute Differences (ROAD) statistic for automatically attenuating or excluding of Gaussian and impulsive ...
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    Experimental Efficiency Analysis in Robust models of Spatial Correlation Optical Flow Methods under Non Gaussian Noisy Contamination 

    Darun Kesrarat; Vorapoj Patanavijit (2013-05)

    In this paper, we present a performance analysis of several robust models of spatial correlation optical flow algorithms including an original spatial correlation optical flow (SCOF), bidirectional for high reliability optical flow (BHR), gradient orientation information for robust motion estimation (GOI), and robust and high reliability based on bidirectional symmetry and median motion estimation (RHR) under the non Gaussian noise conditions. The simulated results are tested on 4 different in foreground and background movement characteristics ...
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    An Experimental Performance Analysis of Image Reconstruction Techniques under Both Gaussian and Non-Gaussian Noise Models 

    Vorapoj Patanavijit (2012-07)

    Recently, the images reconstruction approaches are very essential in digital image processing (DIP), especially in terms of removing the noise contaminations and recovering the content of images. Each image reconstruction approach has different mathematical models. Therefore a performance of individual reconstruction approach is varied depending on several factors such as image characteristic, reconstruction mathematical model, noise model and noise intensity. Thus, this paper presents comprehensive experiments based on the comparisons ...
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    Experimental Study Efficiency of Robust Models of Lucas-Kanade Optical Flow Algorithms in the Present of Non-Gaussian Noise 

    Vorapoj Patanavijit (2012-07)

    This paper presents experimental efficiency study of noise tolerance model of spatial optical flow based on LucasKanade (LK) algorithms such as original LK with kernel of Barron, Fleet, and Beauchemin (BFB), confidence based optical flow algorithm for high reliability (CRR), robust motion estimation methods using gradient orientation information (RGOI), and a novel robust and high reliability for LucasKanade optical flow algorithm using median filter and confidence based technique (NRLK) under several NonGaussian Noise. These experiment ...
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    A fast image recovery using compressive sensing technique with block based orthogonal matching pursuit 

    Vorapoj Patanavijit; Parichat Sermwuthisarn; Supatana Auethavekiat (2009-12)

    Traditionally, the problems of applying Orthogonal Matching Pursuit (OMP) to large images are its high computing time and its requirement for a large matrix. In this paper, we propose a fast image recovery algorithm by dividing the image into block of nxn pixels and applying OMP to each nxn block instead of the entire image. The key idea is that small matrix requires less computing time and less memory. In the experiment, the block based OMP was applied to three standard test images: Lena, Mandrill and Pirate. Compared to standard OMP, block ...
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    Fast Image Restoration Technique for Car License Plate Based on PWS filter Using 2DPCA Algorithm 

    Vorapoj Patanavijit (2013)

    In this paper, we propose the algorithm that used to identify car license plate that the capture images come in degraded version by combining a PWS filtering technique with a 2DPCA algorithm. From experiment results, our algorithm has three advantages. First, it can be operated to the image directly without transforming the structure of the image, which is two dimensional data, into a vector. Second, it can be implemented with less burden of computation and requires less memory. At last, less time is required to restore the image.
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    Multiframe resolution-enhancement using a robust iterative SRR based on leclerc stochastic technique 

    Vorapoj Patanavijit (2009-10)

    This paper proposes a multiframe resolution-enhancement using a robust iterative SRR (Super-Resolution Reconstruction) for applying on images that is corrupted by several nose models. Typically, the success of SRR algorithms is highly dependent on the model accuracy regarding the imaging process. The real noise models corrupting the measure sequence are unknown hence SRR algorithms using L1 or L2 norm may degrade the image sequence rather than enhance it. The proposed enhancement algorithm is based on the stochastic regularization SRR technique ...
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    A Novel Frequency Domain Image Reconstruction Based on the Tikhonov Regularization and Robust Estimation Technique for Compressive Sensing 

    Vorapoj Patanavijit (2013-05)

    Recently, a lot of attention has been paid to image reconstructionalgorithms based on Smoothed L0 (SL0) under the frequency domain. SL0 is fast and accurate under the noise free environment however it is unstable with the additional noise.According to ill-posed condition; without any prior information of the original image, the reconstruction procedure of SL0 is much affected by the noise. The frequency domain Tikhonov reduces and constrains the gap of restored image due to the ill-posed situation. Therefore, image restoration algorithm is ...
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    Performance and Comparative Exploration of Reconstructed Quality for An Iterative SRR Algorithm Based on Robust Norm Functions Under Several Noise Surrounding 

    Vorapoj Patanavijit (2014)

    The multi-frame SRR (Super Resolution Reconstruction) algorithm has become the significant theme in digital image research society in the last ten years because of its performance and its cost effectiveness hence many robust norm functions (both redescending and non-redescending influence functions) have been usually incorporated in the multi-frame SRR framework, which is combined a stochastic Bayesian approach and a regularization technique into the unify SRR framework. Consequently, this paper thoroughly presents experimental exploration ...
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    Performance evaluation of L1, L2 and SL0 on compressive sensing based on stochastic estimation technique 

    Ha, Pham Hong; Vorapoj Patanavijit (2010-05)

    In this paper, we proposed 2 algorithms based on L1 and L2 norm estimator to recover the signal by using just few components. Moreover we comprehensively present the recovery algorithm based on L1, L2 and SL0 for digital signal or image under some several noise models from their incomplete observation. The proposed algorithm is used with a matrix that the number of row is much fewer than the column. The algorithm states that if signal or image is sufficient sparse, we can recover it from small number of linear measurement by solving convex ...
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    A Performance Impact of An Edge Kernel for The High-Frequency Image Prediction Reconstruction 

    Vorapoj Patanavijit; Chaiyod Pirak; Ascheid, Gerd (2014)

    As a rule, the performance of almost digital image processing (DIP) algorithms and these applications directly depends on the spatial resolution of observed input images. Unfortunately, from the current image sensor technology, it is hard to take sufficient high spatial resolution images from commercial devices therefore the fantastic research attempts and, consequently, simple digital image resolution enhancements have been boosted in the last decade. The high-frequency image prediction reconstruction is the simple and effective ...
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    Performance Investigation on Bilateral Filter with Confidence Based over Spatial Correlation-based Optical Flow for Image Reconstruction 

    Darun Kesrarat; Vorapoj Patanavijit (2014)

    Optical flow in image sequences gives essential data on motion structure. It is relevant in several areas such as robot vision, video coding, and super resolution reconstruction. Even though this realm has been concentrated for more than a decade, reduction the glitch in estimation stands a difficult trouble. Many techniques were proposed to enhance the performance and one of the most is bilateral filter. This paper presents the performance study of bilateral filter and bilateral filter together with confidence based over spatial ...
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    Robust compressed sensing in Gaussian noise environment by resampling with replacement 

    Vorapoj Patanavijit; Duangrat Gansawaf; Parichat Sermwuthisarn; Supatana Auethavekiat (2012)

    A reconstruction method using the ensemble of compressed measurement signals is proposed for reconstructing the image from the signal corrupted by Gaussian noise. The ensemble is created from one signal under the assumption that an image is highly redundant; hence, it is approximated as the mixture of a number of signals. The proposed method adopted the sampling with replacement in bootstrapping to extract L signals from the mixture. The extracted L signals from the ensemble of signals corrupted by Gaussian noise with the same mean and variance. ...
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    The Switching Bilateral Denoising Performance Influence of Spatial and Radiometric Variance 

    Kriengkri Langampol; Wilaiporn Lee; Vorapoj Patanavijit (2016-06)

    In this paper, we investigate the performance of switching bilateral filter (SBF) influenced by two parameters — radiometric variance ( R σ ) and spatial variance ( S σ ). The SBF can be used to filter Gaussian noise and impulse noise at the same time. For SBF, R σ and S σ are the two most important factors that affect increasing /decreasing the performance of SBF. Then, we investigate the influence of two parameters by varying their values from 1 to 100 and evaluate the performance of the de-noising for seven types of image under ...
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    Video enhancement using a robust iterative SRR based on andrew's sine regularization technique 

    Vorapoj Patanavijit (2009-12)

    In this paper, we propose a alternative robust video enhancement algorithm using SRR based on the regularization ML technique. First, the classical registration process is used to estimate the relationship between the reference frame and other neighboring frames. Subsequently, the Andrew's Sine norm is used for measuring the difference between the projected estimate of the high quality image and each low high quality image and for removing outliers in the data. Moreover, Tikhonov regularization is incorporated in the proposed framework in order ...
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    Video enhancement using a robust iterative SRR based on a German&McClure stochastic estimation with a general observation model 

    Vorapoj Patanavijit (2010-05)

    This paper proposes the novel robust SRR algorithm that can be effectively applied on the sequence that are corrupted by various noise models and can be applied on the real or standard sequence. First, the proposed SRR algorithm is based on the German&McClure norm that used for measuring the difference between the projected estimate of the high quality image and each low high quality image and for removing outliers in the data. Second, in order to cope with real video sequences and complex motion sequences, the proposed SRR is based on a general ...
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    Video enhancement using a robust iterative SRR based on leclerc stochastic estimation 

    Vorapoj Patanavijit (2009-09)

    Recent results in SRR (Super Resolution Reconstruction) demonstrate that the fusion of a sequence of low-resolution noisy blurred images can produce a higher-resolution image or sequence. Since noise is always present in practical acquisition systems, almost video enhancement algorithms are developed assuming AWGN model for the corruption noise. When the underlying video measurements are corrupted by other noise models such as Poisson Noise, impulsive Noise (Salt & Pepper) and Speckle Noise, the enhancement algorithms fail to recover a close ...