Bicubic interpolation

About: Bicubic interpolation is a research topic. Over the lifetime, 3348 publications have been published within this topic receiving 73126 citations.

Optimal solenoidal interpolation of turbulent vector fields: application to PTV and super-resolution PIV

Abstract: A new approach for the interpolation of a filtered turbulence velocity field given random point samples of unfiltered turbulence velocity data is described. In this optimal interpolation method, the best possible value of the interpolated filtered field is obtained as a stochastic estimate of a conditional average, which minimizes the mean square error between the interpolated filtered velocity field and the true filtered velocity field. Besides its origins in approximation theory, the optimal interpolation method also has other advantages over more commonly used ad hoc interpolation methods (like the ‘adaptive Gaussian window’). The optimal estimate of the filtered velocity field can be guaranteed to preserve the solenoidal nature of the filtered velocity field and also the underlying correlation structure of both the filtered and the unfiltered velocity fields. The a posteriori performance of the optimal interpolation method is evaluated using data obtained from high-resolution direct numerical simulation of isotropic turbulence. Our results show that for a given sample data density, there exists an optimal choice of the characteristic width of cut-off filter that gives the least possible relative mean square error between the true filtered velocity and the interpolated filtered velocity. The width of this ‘optimal’ filter and the corresponding minimum relative error appear to decrease with increase in sample data density. Errors due to the optimal interpolation method are observed to be quite low for appropriate choices of the data density and the characteristic width of the filter. The optimal interpolation method is also seen to outperform the ‘adaptive Gaussian window’, in representing the interpolated field given the data at random sample locations. The overall a posteriori performance of the optimal interpolation method was found to be quite good and hence makes a potential candidate for use in interpolation of PTV and super-resolution PIV data.

An approach of image scaling using DWT and bicubic interpolation

Abstract: Image scaling is an important technique used to scale down or scale up the pictures or video frames to fit to the application. This work proposes a new scaling algorithm for image scaling consisting of a Discrete Wavelet Transform (DWT) based interpolation and bicubic interpolation. To achieve higher visual quality, a simple Haar wavelet based DWT interpolation is carried out first to the gray scale values of image and then bicubic interpolation is performed. DWT is based on sub band coding, which divides the image into four frequency quadrants. To reduce the artifacts, bicubic interpolation is performed to all the quadrants separately. This work can achieve an image quality by a factor more than 10 dB than the existing bilinear interpolation method. The mean square error is less and the average Peak Signal to Noise Ratio (PSNR) is more in this method. The image artifacts like blurring can be greatly reduced in the proposed method, thus this approach is better than existing methods in visual quality. The simulation of the work is carried out in MATLAB R2013a.

Analyzing impact of image scaling algorithms on viola-jones face detection framework

Abstract: In today's world of automation, real time face detection with high performance is becoming necessary for a wide number of computer vision and image processing applications. Existing software based system for face detection uses the state of the art Viola and Jones face detection framework. This detector makes use of image scaling approach to detect faces of different dimensions and thus, performance of image scalar plays an important role in enhancing the accuracy of this detector. A low quality image scaling algorithm results in loss of features which directly affects the performance of the detector. Therefore, in this paper we have analyzed the effect of different image scaling algorithms existing in literature on the performance of the Viola and Jones face detection framework and have tried to find out the optimal algorithm significant in performance. The algorithms which will be analyzed are: Nearest Neighbor, Bilinear, Bicubic, Extended Linear and Piece-wise Extended Linear. All these algorithms have been integrated with the Viola and Jones face detection code available with OpenCV library and has been tested with different well know databases containing frontal faces.

Image compression method combining super-resolution reconstruction

Abstract: The invention discloses a video and image lossy compression method for image coding The method combines traditional JPEG, JPEG2000, H264 and HEVC-code standard algorithms with super-resolution image reconstruction and designs an image compression method combining the super-resolution reconstruction on the basis Downsampling is carried out on an input video and image, wherein a downsampling method adopts a Bicubic algorithm and a downsampling multiple is 2 The number of dot arrays of a downsampling image is only 1/4 of that of an original image The encoding rate of the downsampling image is far lower than that of an original input image so that the encoding rate is reduced At the same time, on the basis that robustness differences of a residual image and a general image are analyzed, a negative-feedback step is introduced in the design so that part of high-frequency detail information lost in a super-resolution image reconstruction step is remedied and reconstructed video or image quality is improved Compared with the JPEG and the H264 standard algorithms, the compression method reduces the encoding rate greatly under a situation that image quality is the same

Smooth Interpolation of Zero Curves

Abstract: Smoothness is a desirable characteristic of interpolated zero curves; not only is it intuitively appealing, but there is some evidence that it provides more accurate pricing of securities. This paper outlines the mathematics necessary to understand the smooth interpolation of zero curves, and describes two useful methods: cubic-spline interpolation—which guarantees the smoothest interpolation of continuously compounded zero rates—and smoothest forwardrate interpolation—which guarantees the smoothest interpolation of the continuously compounded instantaneous forward rates. Since the theory of spline interpolation is explained in many textbooks on numerical methods, this paper focuses on a careful explanation of smoothest forward-rate interpolation.