Subpixel rendering

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

Improving the Spatial Resolution of Land Surface Phenology by Fusing Medium- and Coarse-Resolution Inputs

Abstract: Satellite-derived land surface phenology (LSP) serves as a valuable input source for many environmental applications such as land cover classifications and global change studies. Commonly, LSP is derived from coarse-resolution (CR) sensors due to their well-suited temporal resolution. However, LSP is increasingly demanded at medium resolution (MR), but inferring LSP directly from MR imagery remains a challenging task (e.g., due to acquisition frequency). As such, we present a methodology that directly predicts MR LSP on the basis of the respective CR LSP and MR reflectance imagery. The approach considers information from the local pixel neighborhood at both resolutions by utilizing several prediction proxies, including spectral distance and multiscale heterogeneity metrics. The prediction performs well with simulated data $(R^{2} = 0.84)$ , and the approach substantially reduces noise. The size of the smallest reliably predicted object coincides with the effective CR pixel size (i.e., field-of-view). Nevertheless, even subpixel objects can be reliably predicted provided that pure CR pixels are located within the search radius. The application to real MODIS LSP and Landsat reflectance well preserves the phenological landscape composition, and the spatial refinement is especially striking in heterogeneous agricultural areas, where, for example, the circular shape of center pivot irrigation schemes is successfully restored at MR.

Contextual Subpixel Mapping of Hyperspectral Images Making Use of a High Resolution Color Image

Abstract: This paper describes a hyperspectral image classification method to obtain classification maps at a finer resolution than the image's original resolution. We assume that a complementary color image of high spatial resolution is available. The proposed methodology consists of a soft classification procedure to obtain landcover fractions, followed by a subpixel mapping of these fractions. While the main contribution of this article is in fact the complete multisource framework for obtaining a subpixel map, the major novelty of this subpixel mapping approach is the inclusion of contextual information, obtained from the color image. Experiments, conducted on two hyperspectral images and one real multi source data set, show excellent results, when compared to classification of the hyperspectral data only. The advantage of the contextual approach, compared to conventional subpixel mapping approaches, is clearly demonstrated.

Coupled Bayesian Framework for Dual Energy Image Registration

Abstract: Image registration for X-ray dual energy imaging is challenging due to the overlaid transparent layers (i.e., the bone and soft tissue) and the different appearances between the dual images acquired with X-rays at different energy spectra. Moreover, subpixel accuracy is necessary for good reconstruction of the bone and soft-tissue layers. This paper addresses these problems with a novel coupled Bayesian framework, in which the registration and reconstruction can effectively reinforce each other. With the reconstruction results, we can design accurate matching criteria for aligning the dual images, instead of treating them as multi-modality registration. Furthermore, prior knowledge of the bone and soft tissue can be exploited to detect poor reconstruction due to inaccurate registration; and hence correct registration errors in the coupled framework. A multiscale freeform registration algorithm is implemented to achieve subpixel registration accuracy. Promising results are obtained in the experiments.

A subpixel registration algorithm for low PSNR images

Abstract: This paper presents a fast algorithm for obtaining high-accuracy subpixel translation of low PSNR images. Instead of locating the maximum point on the upsampled images or fitting the peak of correlation surface, the proposed algorithm is based on the measurement of centroid on the cross correlation surface by Modified Moment method. Synthetic images, real solar images and standard testing images with white Gaussian noise added were tested, and the results show that the accuracies of our algorithm are comparable with other subpixel registration techniques and the processing speed is higher. The drawback is also discussed at the end of this paper.

Field programmable object array having image processing circuitry

Abstract: A field programmable object array integrated circuit has video data compression capability. The integrated circuit comprises an array of programmable objects and a video compression co-processor communicatively coupled to the array of objects. The video compression co-processor comprises a set of search engines and a subpixel engine. The subpixel engine can interpolate subpixels from integer pixels and shift the integer pixels by a predetermined number of subpixels. The search engines can perform a plurality of sum of absolute differences (SAD) computations between search window pixels and macroblock pixels to locate the best SAD value using either integer pixels and/or the interpolated subpixels.