Proceeding Papers

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    Decomposing BPC Permutations into Semi- Permutations for Crosstalk Avoidance in Multistage Optical Interconnection Networks
    This paper introduces a simple O(N) algorithm that decomposes BPC (bit-permutecomplement) permutations into semi-permutations for avoiding crosstalk when realizing them in N × N optical multistage interconnection networks (OMINs). Crosstalk means that two optical signals, sharing an optical switch, undergo a kind of undesired coupling. A semipermutation is a partial permutation which meets the requirement for each switch in an input and output stages of the network to be used with only one optical signal at a time. It provides avoiding crosstalk in the first and the last stages of a network and creates the potential for crosstalk-free realization of a semi-permutation, and finally the whole permutation in question. The algorithm is based on employment the periodicity of appearing 1’s and 0’s in columns of transition matrices for BPC permutations.
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    Bidirectional Confidential with Bilateral Filter on Local Based Optical Flow for Image Reconstruction under Noisy Condition
    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 the quality of the restored images from the returned motion vector is focal point. Several noise levels over several official sequences are observed for the noise tolerance analysis where PSNR is used as an indicator.
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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
    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 of an iterative SRR algorithm based on several robust norm functions such as zero-redescending influence functions (Tukey’s Biweight, Andrew’s Sine and Hampel), nonzero-redescending influence functions (Lorentzian, Leclerc, Geman&McClure, Myriad and Meridian) and nonredescending influence functions (Huber). This paper utilizes two standard images of Lena and Susie (40th) for pilot studies and fraudulent noise patterns of AWGN, Poisson, Salt&Pepper, and Speckle of several magnitudes are used to contaminate these two standard images. The comparative experiment has been done by thoroughly changing all parameters such as step-size, regularization parameter, norm constant parameter in order to obtain the maximum PSNR (peak-signal-to-noise ratio).
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    Performance Investigation on Bilateral Filter with Confidence Based over Spatial Correlation-based Optical Flow for Image Reconstruction
    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 correlationbased optical flow where the quality of the reconstructed images from the result of motion vector is highly focused. We perform an experiment over several standard sequences with several noise levels in contamination to study the robustness of each robust technique and used PSNR as an indicator.
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    A Performance Impact of An Edge Kernel for The High-Frequency Image Prediction Reconstruction
    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 algorithm for enhancing the image resolution however this algorithm is strongly depends on the edge detection kernel and M0 parameter. Therefore, this paper studies a performance impact of an edge detection kernel such as Roberts kernel, Prewitt Kernel, Sobel Kernel, Laplacian Kernel and Laplacian of Gaussian (LOG) Kernel for the high-frequency image prediction reconstruction. This paper presents three experimental performance studies under a noiseless environment, several blurred environments at different blurred variance and several noisy environments at different noise power levels. The first performance study is an empirical exhaustive study of an optimal edge detection kernel and the study of optimal M0 value is experimentally determined under this environment. The second performance study is an empirical exhaustive study of an optimal edge detection kernel and the study of optimal M0 value is experimentally determined under these environments. Finally, the last performance study is an empirical exhaustive study of an optimal edge detection kernel and the study of optimal M0 value is experimentally determined under these environments.