Simple Adaptive PI Control for Linear Time-Delay Systems
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A variety of image interpolation methods have been used in order to obtain high-resolution (HR) images. In this paper, we propose an advanced bilinear interpolation algorithm which improves edge components. The conventional bilinear image interpolation method has a serious problem such as blurring artifacts. In the experimental results, our proposed algorithm outperforms the bilinear image interpolation not only objective qualities but also subjective qualities.
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The authors discuss the applicability of the Tchebycheff iteration scheme and a simplified derivative of this scheme in electronic calculations. Both methods are very simple and lead to a strongly accelerated convergence especially in cases where the simple mixing scheme converges quite slowly.
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The single image super-resolution issue is studied in this paper. We propose a new method based on a combination of bilinear interpolation and the U-Net neural network to solve this problem. The image is enlarged by bilinear interpolation, then its quality is improved by the neural network. This approach allows improving reconstruction quality by 1.00-6.56% compared with known methods.
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Bilinear interpolation magnification can make edge blur while smoothing images.This paper puts forward an enhanced image outline optimization algorithm based on bilinear interpolation.Experimental analysis showes that optimization algorithm is more suitable for the real-time digital image processing amplification.
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Bilinear interpolation is widely used to process digital image.Its continuity is better than Nearest neighbor interpolation,but is time-consuming.In order to speed up,a discrete bilinear interpolation algorithm is proposed.First,pixel is cut into 3x3 sub-pixel.Then the center value of each sub-pixel with bilinear interpolation function is calculated,we can get a convolution function C related with each sub-pixel,the function C can be a substitute of bilinear interpolation.Theoretical analyses show that the discrete algorithm decreases the times of floating addition and floating multiplication.Experiments of image rotation show that the discrete algorithm improve the per-formance more greatly than that of bilinear interpolation.The quality of image rotated by the discrete algorithm is almost equal to that by the bilinear interpolation except for a little loss of continuity.
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In this paper, we present an interpolation scheme for FRQI images based on bilinear interpolation. To accomplish this, we formulated several quantum modules, i.e., assignment module, increment module, and quarter module, and suffused them into our proposed quantum image interpolation circuit. The concrete quantum circuits to accomplish up-scaling and down-scaling based on bilinear for FRQI images are designed and the network complexities of them are analyzed. Finally, to validate the proposed method, simulation experiments to enlarge and reduce the test images are executed, whose results are compared with the nearest neighbor interpolation for FRQI images. The up-scaled images by using proposed interpolation algorithm achieve satisfactory results, and both PSNR and SSIM values are better than those of the nearest neighbor method.
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In this paper, we propose a new interpolation algorithm based on edge-weighted method, which improves the bilinear interpolation method. The conventional bilinear interpolation has a serious problem which is the blurring artifact crumbling the edge components. In this paper, the edge-weighted image (EWI) interpolation algorithm, which enhances the edge components, is proposed. In the experimental results, not only did the EWI interpolation exhibit much better subjective qualities of the images than conventional bilinear interpolation, but also improved objective qualities.
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Remote sensing image interpolation requires preserving the information of source image as much as possible, improving the image resolution, being appropriate for people to observe, and so forth. Presents wavelet bilinear interpolation algorithm, which is suit to remote sensing image. It has been found that the algorithm can obtain higher SNR and better image effect compared with bilinear interpolation.
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In this study, a new image interpolation approach using the visual attention model is proposed. First, the high-quality saliency map of an image to be interpolated is generated in an effective manner. Then based on the saliency map, the bilinear interpolation method and a proposed interpolation method are employed for non-saliency blocks and saliency blocks, respectively, to obtain the final interpolation results. Based on the experimental results obtained in this study, the interpolation results of the proposed method are better than the corresponding results of the three comparison methods.
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When we generated image by the traditional bilinear interpolation , constantly call the formula calculated for each pixel color value, leading to the speed of the image generated slowly.This article describes the bilinear interpolation algorithm based on double iterative method, thus reducing the computational complexity increases the speed of generating the image.
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