Learning-based video super-resolution reconstruction using particle swarm optimization.
ABSTRACT In this study, a learning-based video super-resolution (SR) reconstruction approach using particle swarm optimization (PSO) is proposed. First, a 5×5×5 motion-compensated volume containing five 5×5 motion-compensated patches is extracted and the orientation of the volume is determined for each pixel in the “central” reference low-resolution (LR) video frame. Then, the pixel values of the “central” reference high-resolution (HR) video frame are reconstructed by using the corresponding SR reconstruction filtering masks, based on the orientation of the volume and the coordinates of the pixels to be reconstructed. To simplify the PSO learning processes for determining the weights in SR reconstruction filtering masks, simple mask flippings are employed. Based on the experimental results obtained in this study, the SR reconstruction results of the proposed approach are better than those of three comparison approaches, whereas the computational complexity of the proposed approach is higher than those of two “simple” comparison approaches, NN and Bicubic, and lower than that of the recent comparison approach, modified NLM.
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ABSTRACT: A new approach toward increasing spatial resolution is required to overcome the limitations of the sensors and optics manufacturing technology. One promising approach is to use signal processing techniques to obtain an high-resolution (HR) image (or sequence) from observed multiple low-resolution (LR) images. Such a resolution enhancement approach has been one of the most active research areas, and it is called super resolution (SR) (or HR) image reconstruction or simply resolution enhancement. In this article, we use the term "SR image reconstruction" to refer to a signal processing approach toward resolution enhancement because the term "super" in "super resolution" represents very well the characteristics of the technique overcoming the inherent resolution limitation of LR imaging systems. The major advantage of the signal processing approach is that it may cost less and the existing LR imaging systems can be still utilized. The SR image reconstruction is proved to be useful in many practical cases where multiple frames of the same scene can be obtained, including medical imaging, satellite imaging, and video applications. The goal of this article is to introduce the concept of SR algorithms to readers who are unfamiliar with this area and to provide a review for experts. To this purpose, we present the technical review of various existing SR methodologies which are often employed. Before presenting the review of existing SR algorithms, we first model the LR image acquisition process.IEEE Signal Processing Magazine 06/2003; 20(3-20):21 - 36. DOI:10.1109/MSP.2003.1203207 · 5.85 Impact Factor
Conference Paper: A New Optimizer Using Particle Swarm Theory[Show abstract] [Hide abstract]
ABSTRACT: The optimization of nonlinear functions using particle swarm methodology is described. Implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm. Benchmark testing of both paradigms is described, and applications, including neural network training and robot task learning, are proposed. Relationships between particle swarm optimization and both artificial life and evolutionary computation are reviewedMicro Machine and Human Science, 1995. MHS '95., Proceedings of the Sixth International Symposium on; 11/1995
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ABSTRACT: Super-resolution reconstruction of image sequences is highly dependent on data outliers and on the quality of the motion estimation. This paper addresses the design of the least mean square algorithm applied to super-resolution reconstruction (LMS-SRR). Based on a statistical model for the algorithm behavior, we propose a design strategy to reduce the effects of outliers on the reconstructed image sequence. We show that the proposed strategy leads the algorithm to a close-to-optimum performance in both the transient and the steady-state phases of adaptation in practical situations in which registration errors occur. The analysis also shows that lower values of the step size do not necessarily lead to a better steady-state mean-square error, differently from the traditional LMS behavior.IEEE Transactions on Signal Processing 03/2008; 56(2-56):555 - 564. DOI:10.1109/TSP.2007.907910 · 2.79 Impact Factor