A Switching Based Adaptive Image Interpolation

2011

This paper presents a new image interpolation technique for enhancement of spatial resolution of images. The proposed algorithm uses the switching of existing Soft-decision Adaptive Interpolation (SAI) algorithm and Single Pass Interpolation Algorithm (SPIA) methods. We learn the error pattern in the interpolation process of SAI method and SPIA Method after interpolating downsampled version of LR image. Then we deviced a mechanism to correct the error pattern. Emperically we found that SAI methods works better on smooth 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 interpolation. So, a hybrid scheme of combining SAI method and SPIA method is proposed for best prediction of high resolution (HR) image. The proposed algorithm produces the best results in different varieties of images in terms of both PSNR measurement and subjective visual quality.

IJETAE, 2013

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).

2011 IEEE International Instrumentation and Measurement Technology Conference, 2011

In this paper we proposed a new computationally efficient interpolation algorithm for natural images in which unknown pixels are divided into few bins. The categorization of these unknown pixels into bins is based upon the characteristics of the neighboring pixels. These characteristics are obtained by taking difference of two slopes which are in orthogonal direction and these slopes are calculated from a set of neighboring pixels. We used the Least-Squares (LS) based approach to find optimal predictors for pixels belonging to various slope bins. We also presented a simplified proposed algorithm in which we used bilinear interpolation algorithm instead of estimating LS based predictor for some bins and it results into further reduction in computational complexity without sacrificing the much performance. Our proposed algorithm gives better interpolation quality with significantly lower computational complexity as compared to recently reported interpolation algorithms.

2009

Image resampling is a process which change the size of an image. It generates output image of the specified dimension with the help of the interpolation techniques. The up-sampled images are lost fine detailed information due to artifacts. This is a problem to define detail content clearly. This is a challenge to restore the lost information. Many restoration methods have been developed The proposed framework is based on non-adaptive interpolation method. Non-adaptive method is faster than adaptive one. This framework can interpolate and produce good image with fine detailed information. This approach can assit in photo editing process and other image processing application.

Journal of Visual Communication and Image Representation, 1997

Blurring and jaggedness are the two most obvious arti-This paper presents a novel edge preserving interpolation facts in the interpolated images using the preceding intermethod for digital images. This new method reduces drastically polation methods. These artifacts are produced by the lowthe blurring and jaggy artifacts at the high-contrast edges, pass filter used to remove the unwanted highpass replica which are generally found in the interpolated images using of the interpolated images in the frequency domain. Beconventional methods. This high performance is achieved by cause it is not possible to implement the ideal low pass two proposed operations: a fuzzy-inference based edge preservfilter in practice, nonideal filters such as the zero-order ing interpolator and a highly oblique edge compensation scheme hold (nearest neighbor) and the first-order hold (bilinear) developed based on an edge orientation detector. The former operators are often employed to filter out the high fresynthesizes the interpolated pixels to match the image local quency replica. These nonideal lowpass filters suppress characteristics. Hence, edge sharpness can be retained. Howlow frequency components and bring in high frequency ever, due to the small footage of the fuzzy interpolation method, it cannot avoid edge jaggedness along the highly oblique edges component aliasing. The former, low frequency suppresthat have very sharp angles against one of the coordinates. sion, reduces the spatial resolution of the interpolated im-Therefore, a segment matching technique is developed to idenages (blurring) and the latter, undesired high frequency tify precisely the orientation of the highly oblique edges. Comaliasing, produces broken edges (jaggedness). bining these two techniques, we improve significantly the visual Recently, several adaptive nonlinear methods have been quality of the interpolated images, particularly at the highsuggested to tackle the aforementioned problems. As it contrast edges. Both the synthesized images (such as letters) has been pointed out to various levels of extent [8-13] that and the natural scenes (captured by camera) have been tested in order to achieve a better visual quality, the lost high and the results are very promising.