The phase holds crucial information about image structures and features, but only the real part or the magnitude of the transform coefficients is often used for image processing applications. In this paper, a method for the feature extraction of images called Phase-based LBP is presented. Proposed method is based on the combination of phase of complex wavelet coefficients and the Local Binary Pattern operator (LBP). We also perform the comparative analysis about retrieval effectiveness of phase information of some complex wavelet transforms using for Phase-based LBP. Experimental results, achieved with the standard rotated Brodatz dataset, show the interest of this method comparing with another methods only based on the real part or the magnitude of the wavelet coefficients for texture image retrieval.
How to reconstruct a high resolution and quality from low resolution images captured from a digital camera is always the top target of any image processing system. Exploit the aliasing feature of sampled images, we propose a new technique to register exactly the motions between images, including rotations and shifts, by using only frequency domain phase-shift method. Based on registered parameters, the precise alignment of input images are done to create a high-resolution by using interpolation methods. It is more exactly when we compare our algorithm to other algorithms in simulation and practical experiments. The visual results of super-resolution images reconstruced by our algorithm are better than that of other algorithms. It is possible to apply our algorithm to increase resolution of digital camera or video systems.
The bag-of-words (BoW) model is used widely for image classification. In this model, the image-level representations are designed using BoW frameworks from local low-level features, therefore we introduce our local low-level feature, called the denseSBP feature, using for BoW. We will evaluate performance in classification when using this feature. To increase average precision, we combine denseSBP feature with other features using Multiple Kernel Learning (MKL). In this work, we also propose the method called the integrated method, that it based on using multi-features and multi-kernels in SVM classification to derive the best classification accuracy for each category of a dataset. We perform the comparative analysis about classification accuracies of the method using MKL and the integrated method on image benchmark datasets. The experimental results show comparable classification accuracies of proposal methods with the state-of-the-art methods.
Multi-frame super-resolution brings out much potential to reconstruct real high-resolution video sequences. This potential is achieved based on its capacity to combine missing information from different input low-resolution frames. Although there have been many studies in recent decades, superresolution problems for real-world video processing still have many challenges. This is dues to two problems of: how to address the affecting factors: motion, sampling and noise explicitly and how to solve them exactly and efficiently. This paper introduces an efficient approach for video super-resolution by addressing real motion, sampling and noise models. Based on that, we proposed a model for receiving a practical video and an efficient framework to estimate adaptively the motion and noise to reconstruct the original high-resolution frames. Our system achieves promising results when compare with other state-of-theart in quality and processing time.
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