Bicubic interpolation

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

A Review on Different Image Interpolation Techniques for Image Enhancement

Abstract: Image enhancement is an important processing task in image processing field. By applying image enhancement, blur or any type of noise in the image can be removed so that the resultant image quality is better. Image enhancement is used in various fields like medical diagnosis, remote sensing, agriculture, geology, oceanography. There are numbers of techniques for image enhancement. Image interpolation is used to do enhancement of any image. This paper gives overview about different interpolation techniques like nearest neighbor, bilinear, bicubic, new edge-directed interpolation (NEDI), data dependent triangulation (DDT), and iterative curvature-based interpolation (ICBI).

Rendering cubic curves and surfaces with integer adaptive forward differencing

Abstract: For most compute environments, adaptive forward differencing is much more efficient when performed using integer arithmetic than when using floating point. Previously low precision integer methods suffered from serious precision problems due to the error accumulation inherent to forward differencing techniques. This paper proposes several different techniques for implementing adaptive forward differencing using integer arithmetic, and provides an error analysis of forward differencing which is useful as a guide for integer AFD implementation. The proposed technique using 32 bit integer values is capable of rendering curves having more than 4K forward steps with an accumulated error of less than one pixel and no overflow problems. A hybrid algorithm employing integer AFD is proposed for rendering antialiased, texture-mapped bicubic surfaces.

Shape design sensitivity and optimization of anisotropic functionally graded smart structures using bicubic B-splines DRBEM

Abstract: A new shape optimization technique is developed, using bicubic B-splines dual reciprocity boundary element method, for anisotropic functionally graded smart structures to minimize weight while satisfying certain constraints upon stresses and geometry. An implicit differentiation of the boundary integral equation with respect to geometric design variables is used to calculate shape design sensitivities of anisotropic materials. This method allows the coupling of an optimizing technique and a boundary element elastic stress analyzer to form an optimum shape design algorithm in two dimensions and also allows high-accuracy computation. The boundary element method needs much fewer data related only to the considered boundary of the structure, so it is very suitable for shape optimization in comparison with the finite element method. Because of the non-linear behavior of weight and stresses, the numerical optimization method used in the program is the feasible direction approach, together with the one-dimensional golden-section search technique. The two-dimensional electric fillet knife used as the numerical example in order to verify the formulation and the implementation of the proposed method.

Rotation of NMR images using the 2D chirp-z transform.

Abstract: A quick and accurate way to rotate and shift nuclear magnetic resonance (NMR) images using the two-dimensional chirp-z transform is presented. When the desired image grid is rotated and shifted from the original grid due to patient motion, the chirp-z transform can reconstruct NMR images directly onto the ultimate grid instead of reconstructing onto the original grid and then applying interpolation to get the final real-space image in the conventional way. The rotation angle and shift distances are embedded in the parameters of the chirp-z transform. The chirp-z transform implements discrete sinc interpolation to get values at grid points that are not exactly on the original grid when applying the inverse Fourier transform. Therefore, the chirp-z transform is more accurate than methods such as linear or bicubic interpolation and is more efficient than direct implementation of sinc interpolation because the sinc interpolation is implemented at the same time as reconstruction from k-space.

Regularity-preserving image interpolation

Abstract: Common image interpolation methods assume that the underlying signal is continuous and may require that it possess one or more continuous derivatives. These assumptions are not generally true of natural images, most of which have instantaneous luminance transitions at the boundaries between objects. Continuity requirements on the interpolating function produce interpolated images with oversmoothed edges. To avoid this effect, a wavelet-based interpolation method that imposes no continuity constraints is introduced. The algorithm estimates the regularity of edges by measuring the decay of wavelet transform coefficients across scales and attempts to preserve the underlying regularity by extrapolating a new subband to be used in image resynthesis. The algorithm produces noticeably sharper edges than traditional techniques and exhibits an average PSNR improvement of 2.5 dB over bilinear and bicubic techniques.