Papers by Keyword: Image Interpolation

Abstract: This paper presents an improvement fast image interpolation algorithm, which we divided the low resolution images into smooth area, edge area and texture area based on threshold control mode, then we using three channel to achieve fast interpolation. Experiments show that this method makes the image texture details clear, won the high resolution image.

Abstract: This paper presents a novel approach of image interpolation based on the switching of new edge directed interpolation (NEDI) and single pass interpolation algorithm ( SPIA ) and switching is based upon the % of edges present in the blocks of the image. The switching of this interpolation algorithm is block based instead of image based or pixel based. Imperially we found that NEDI methods is better applicable for smoother images (variation among the pixels is less) while SPIA method works better on detailed images (more variation among the pixels), because of the type of pixels used in the process interpolation. So, a hybrid scheme of combining NEDI method and SPIA method is used for better prediction of HR image. The proposed algorithm produces the better results for different varieties of images in terms of both PSNR measurement and subjective visual quality with low computational complexity as compare to recently developed interpolation algorithms.

Abstract: A novel algorithm is introduced in this paper which gives the spectrum characteristics of an image by analyzing the periodicity of interpolating signal and then estimate scaling ratio of the image. In this paper we also compare the spectrum characteristics of each block after partitioning an image into small overlapping blocks for judging whether the image was spliced. Moreover, tampered regions can be identified. Experiment results show that this algorithm can detect whether a photograph captured by the digital camera is undergone the digital zoom. At the same time it gives the estimation of scaling ratio. As well as, it is efficient to detecting the re-sampled image.

Abstract: The traditional image denoising methods could effectively remove the noise. But the useful information in the detail abundant areas would be thrown off, and the edge appears mistiness. The search for efficient image denoising methods is still a valid challenge in image processing. This paper proposes a method for removing noise with keeping image detail in smoothness areas and detail abundant areas. The main focus of this paper is to define a general mathematical and experimental methodology to compare and classify classical image denoising algorithms. A bivariate rational interpolation with parameters is used in the algorithm. An interpolation surface is constructed using an image data as the interpolation data. According to the maximum and minimum membrane energy value of the interpolation surface, the noise pixel is detected. If it is a noise point, the value is replaced by the rank-ordered mean of the filter window and the membrane energy during noise removal. The experimental results demonstrate that the proposed method outperforms other conventional methods and recently proposed methods in reducing noise and retaining details.

Abstract: Image interpolation is used widely in the computer vision. Holding edge information is main problem in the image interpolation. By using bilinear and bicubic B-spline interpolation methods, a novel image interpolation approach was proposed in this paper. Firstly, inverse distance weighted average method was used to reduce image’s noise. Secondly, edge detection operator was used to extract image's edges information. It can help us to select different interpolation methods in the image interpolation process. Finally, we selected bilinear interpolation approach at non-edge regions, and bicubic B-spline interpolation method was used near edges regions. Further more, control vertexes were computed from pixels with calculation formula which has been simplified in the B-spline interpolation process. Experiments showed the interpolated image by the proposed method had good vision results for it could hold image's edge information effectively.

Abstract: In the field of experimental mechanics, there exist some circumstances when only data at the boundary can be obtained while the internal data are unavailable, or when some data are missed due to shadow, illumination saturation and other reasons. Thus it would be helpful if a reasonable estimation of the unavailable or missed data can be obtained. In this study, an algorithm is developed to reconstruct the missed data from the existing ones by generating a series of equations about the missed data and solving for an optimal solution using least-squares approach. Results based on both simulation data and real incomplete experimental data obtained by shearography and fringe projection show the usefulness and potential of the algorithm for experimental mechanics applications.

Abstract: A new nonlinear intensity interpolation algorithm is presented to realize sub-pixel edge detection. The interpolation algorithm based on the Canny criteria makes full use of grads information attained by Canny edge detection to perform special interpolation in the grads direction. When the resolution is enhanced, the interpolated image by the new interpolation scheme can efficiently preserve high frequency component in the original image. The edge detection of interpolated image permits high precision localization. The new interpolation algorithm is more effective in reserving the grads information of the step edge of the initial image than the usual linear interpolations. It requires simpler computation than the present non-linear interpolations.