A survey on image interpolation methods

Abstract: 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.

Abstract: Image interpolation is a method of obtaining a high resolution image from a low resolution image, which is applied to many image processing procedures In order to make the interpolated image having smooth edges and make the interpolation processing fast, we propose a fast edge-directed interpolation algorithm in this paper The proposed method consists of three steps, the determination of nonedge pixels and edge pixels, the bilinear interpolation for nonedge pixels, and the edge-adaptive interpolation for edge pixels The experimental results show that it outperforms some existing interpolation algorithms in terms of image quality and processing speed

Abstract: 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.

Abstract: Interpolation is the most executed operation and one of the main bottlenecks in embedded imaging, registration, and rendering systems. Existing methods either lack parallelization and scalability capabilities or are too computationally complex to execute efficiently. Acknowledging that improving execution time leads to degradation in image quality, we formulate a novel Negative Squared Distance (NSD) interpolation method that exhibits excellent performance by exploiting Look-Up Table (LUT) optimization for Field Programmable Gate Array (FPGA) speedup, with a balanced trade-off in quality in our embedded endomicroscopic imaging system. Quantitative analysis on performance and resource utilization of NSD against existing methods is reported through an implementation on a Xilinx ML605 platform. Functional validation using practical image resizing and rotation applications to compare qualitative performance against existing algorithms is performed and presented with visual and numerical results. Our method is shown to have a smaller design size and produces a maximum throughput of over twofold against trilinear interpolation with on-par image quality as the baseline method.