Subpixel rendering

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

Measurement of the presampled two-dimensional modulation transfer function of digital imaging systems.

Abstract: The purpose of this work was to develop methods to measure the presampled two-dimensional modulation transfer function (2D MTF) of digital imaging systems A custom x-ray "point source" phantom was created by machining 256 holes with diameter 0107 mm through a 05-mm-thick copper plate The phantom was imaged several times, resulting in many images of individual x-ray "spots" The center of each spot (with respect to the pixel matrix) was determined to subpixel accuracy by fitting each spot to a 2D Gaussian function The subpixel spot center locations were used to create a 5 x oversampled system point spread function (PSF), which characterizes the optical and electrical properties of the system and is independent of the pixel sampling of the original image The modulus of the Fourier transform of the PSF was calculated Next, the Fourier function was normalized to the zero frequency value Finally, the Fourier transform function was divided by the first-order Bessel function that defined the frequency content of the holes, resulting in the presampled 2D MTF The presampled 2D MTF of a 01 mm pixel pitch computed radiography system and 02 mm pixel pitch flat panel digital imaging system that utilized a cesium iodide scintillator was measured Comparison of the axial components of the 2D MTF to one-dimensional MTF measurements acquired using an edge device method demonstrated that the two methods produced consistent results

Temporal target tracking in hyperspectral images

Abstract: This research addresses the problem of tracking a moving point target in a time sequence of hyperspectral images; we focus on the detection of moving targets with staring technologies In these applications, the images consist of targets moving at subpixel velocity in backgrounds that are influenced by both evolving clutter and noise The demand for a low false-alarm rate on one hand and a high probability of detection on the other makes the tracking a challenging task The use of hyperspectral images should be superior to current technologies, due to the benefit of simultaneously exploiting two target-specific properties: the spectral target characteristics and the time-dependent target behavior We propose an algorithm that is in two steps The first step is the transformation of each of the hyperspectral images forming the sequence into a two-dimensional image using a known point-target detection-acquisition algorithm In the second step, target detection and tracking are performed by the means of time-domain processing A match-filter technique is used for the hyperspectral image transformation; a variance-filter algorithm is developed to detect the presence of targets from the temporal profile of each pixel while suppressing clutter-specific influences

Organic light-emitting display and fabricating method thereof

Abstract: An organic light-emitting display (OLED) is provided. The OLED comprises a plurality of pixels on a substrate. Each pixel on the substrate comprises a first electrode, an organic luminescent layer and a second electrode in sequence. The first electrode comprises a first subpixel region, a second subpixel region and a third subpixel region. The organic luminescent layer comprises a first organic luminescent layer and a second organic luminescent layer. The first organic luminescent layer is above the first subpixel region and the second subpixel region. The second organic luminescent layer is above the second subpixel region and the third subpixel region. The fabricating method of the OLED is disclosed in the specification too.

Empirical filter estimation for subpixel interpolation and matching

Abstract: We study the low-level problem of predicting pixel intensities after subpixel image translations. This is a basic subroutine for image warping and super-resolution, and it has a critical influence on the accuracy of subpixel matching by image correlation. Rather than using traditional frequency-space filtering theory or ad hoc interpolators such as splines, we take an empirical approach, finding optimal subpixel interpolation filters by direct numerical optimization over a large set of training examples. The training set is generated by subsampling larger images at different translations, using subsamplers that mimic the spatial response functions of real pixels. We argue that this gives realistic results, and design filters of various different parametric forms under traditional and robust prediction error metrics. We systematically study the performance of the resulting filters, paying particular attention to the influence of the underlying image sampling regime and the effects of aliasing ("jaggies"). We summarize the results and give practical advice for obtaining subpixel accuracy.

Weighting Function Alternatives for a Subpixel Allocation Model

Abstract: This study investigates the “pixel-swapping” optimization algorithm proposed by Atkinson for predicting subpixel landcover distribution. Two limitations of this method are assessed: the arbitrary spatial range value and the arbitrary exponential model for characterizing spatial autocorrelation. Various alternative weighting functions are evaluated. For this assessment, two different simulation models are employed to develop spatially autocorrelated binary class raster maps. These rasters are then resampled to generate sets of representative medium-resolution class maps. Prior to conducting the subpixel allocation, the relationship between cell resolution and spatial autocorrelation, as measured by Moran’s I, is evaluated. It is discovered that the form of this relationship depends upon the simulation model. For all tested weighting functions (Nearest Neighbor, Gaussian, Exponential, and IDW), the pixel swapping method increased classification accuracy compared with the initial random allocation of subpixels. Nearest Neighbor allocation performs as well as the more complex models of spatial structure.