Subpixel rendering

About: Subpixel rendering is a research topic. Over the lifetime, 3885 publications have been published within this topic receiving 82789 citations.

OS-PC: Combining Feature Representation and 3-D Phase Correlation for Subpixel Optical and SAR Image Registration

Abstract: Phase correlation (PC), an efficient frequency-domain registration method, has been extensively used in remote sensing images owing to its subpixel accuracy and robustness to image contrast, noise, and occlusions. However, its performance becomes poor when applied to the registration between optical and synthetic aperture radar (SAR) images, which are two typical multisensor images. Inspired by the recently proposed feature-based methods, we present a novel subpixel registration method that combines robust feature representations of optical and SAR images and the 3-D PC (OS-PC). The robust feature representations, which capture the inherent property of the two images and retain their structural information, form two dense image cubes. The 3-D PC utilizes the image cubes as a substitute of two raw images to estimate 2-D translations, either by locating peak in the spatial domain or by directly working in the Fourier domain. Furthermore, we investigate two techniques to improve the accuracy of the 3-D PC both in the spatial domain and Fourier domain: the first is the constrained energy minimization method to seek the Dirac delta function after 3-D inverse Fourier transform and the second is the fast sample consensus fitting to estimate phase difference after high-order singular value decomposition of the PC matrix. Experiments with both simulated and satellite optical-to-SAR pairs were carried out to test the proposed method. Compared with state-of-the-art PC methods and optical-to-SAR registration methods, the proposed method presents a superior performance in both accuracy and robustness. Moreover, we verify the adaptability of the proposed method.

Measuring Coseismic Displacements With Point-Like Targets Offset Tracking

Abstract: Offset tracking is an important complement to measure large ground displacements in both azimuth and range dimensions where synthetic aperture radar (SAR) interferometry is unfeasible. Subpixel offsets can be obtained by searching for the cross-correlation peak calculated from the match patches uniformly distributed on two SAR images. However, it has its limitations, including redundant computation and incorrect estimations on decorrelated patches. In this letter, we propose a simple strategy that performs offset tracking on detected point-like targets (PT). We first detect image patches within bright PT by using a sinc-like template from a single SAR image and then perform offset tracking on them to obtain the pixel shifts. Compared with the standard method, the application on the 2010 M 7.2 El Mayor-Cucapah earthquake shows that the proposed PT offset tracking can significantly increase the cross-correlation and thus result in both efficiency and reliability improvements.

Hybrid Constraints of Pure and Mixed Pixels for Soft-Then-Hard Super-Resolution Mapping With Multiple Shifted Images

Abstract: Multiple shifted images (MSIs) have been widely applied to many super-resolution mapping (SRM) approaches to improve the accuracy of fine-scale land-cover maps. Most SRM methods with MSIs involve two processes: subpixel sharpening and class allocation. Complementary information from the MSIs has been successfully adopted to produce soft attribute values of subpixels during the subpixel sharpening process. Such information, however, is not used in the second process of class allocation. In this paper, a new class-allocation algorithm, named “hybrid constraints of pure and mixed pixels” (HCPMP), is proposed to allocate land-cover classes to subpixels using MSIs. HCPMP first determines the classes of subpixels that overlap with the pure pixels of auxiliary images in MSIs, after which the remaining subpixels are classified using information derived from the mixed pixels of the base image in MSIs. An artificial image and two remote sensing images were used to evaluate the performance of the proposed HCPMP algorithm. The experimental results demonstrate that HCPMP successfully applied MSIs to produce SRM maps that are visually closer to the reference images and that have greater accuracy than five existing class-allocation algorithms. Especially, it can produce more accurate SRM maps for high-resolution land-cover classes than low-resolution cases. The algorithm takes slightly less runtime than class allocation using linear optimization techniques. Hence, HCPMP provides a valuable new solution for class allocation in SRM using auxiliary data from MSIs.

System and method for improving sub-pixel rendering of imaging data in non-striped display systems

Abstract: The present application discloses a number of embodiments for the mapping of input image data onto display panels in which the subpixel data format being input may differ from the subpixel data format suitable for the display panel. Systems and methods are disclosed to map input image data onto panels with different ordering of subpixel data that the input, different number of subpixel data sets or different number of color primaries that the input image data.

Subpixel edge detection and estimation with a microprocessor-controlled line scan camera

Abstract: A microprocessor-controlled line scan camera system for measuring edges and lengths of steel strips is described, and the problem of subpixel edge detection and estimation in a line image is considered. The edge image is assumed to change gradually in its intensity, and the true edge location may be between pixels. Detection and estimation of edges are based on measurement of gray values of the line images at a limited number of pixels. A two-stage approach is presented. At the first stage, a computationally simple discrete-template-matching method is used to place the estimated edge point to the nearest pixel value. Three second-stage methods designed for subpixel estimation are examined. The modified Chebyshev polynomial and the three-point interpolation method do not require much knowledge on the shape of the edge intensity. If the functional form of the edge is known, a least-square estimation method may be used for better accuracy. In the case of nonstationary Poisson noise, a recursive maximum-likelihood method for the first-stage edge detection, followed by subpixel estimation, is proposed. >