Subpixel rendering

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

How to design synthetic images to validate and evaluate hyperspectral imaging algorithms

Abstract: Many hyperspectral imaging algorithms are available for applications such as spectral unmixing, subpixel detection, quantification, endmember extraction, classification, compression, etc and many more are yet to come. It is very difficult to evaluate and validate different algorithms developed and designed for the same application. This paper makes an attempt to design a set of standardized synthetic images which simulate various scenarios so that different algorithms can be validated and evaluated on the same ground with completely controllable environments. Two types of scenarios are developed to simulate how a target can be inserted into the image background. One is called Target Implantation (TI) which implants a target pixel by removing the background pixel it intends to replace. This type of scenarios is of particular interest in endmember extraction where pure signatures can be simulated and inserted into the background with guaranteed 100% purity. The other is called Target Embeddedness (TE) which embeds a target pixel by adding this target pixel to the background pixel it intends to insert. This type of scenarios can be used to simulate signal detection models where the noise is additive. For each of both types three scenarios are designed to simulate different levels of target knowledge by adding a Gaussian noise. In order to make these six scenarios a standardized data set for experiments, the data used to generate synthetic images can be chosen from a data base or spectral library available in the public domain or websites and no particular data are required to simulate these synthetic images. By virtue of the designed six scenarios an algorithm can be assessed objectively and compared fairly to other algorithms on the same setting. This paper demonstrates how these six scenarios can be used to evaluate various algorithms in applications of subpixel detection, mixed pixel classification/quantification and endmember extraction.

Display method of emission display apparatus

Abstract: Sticking of a Pixel is suppressed to improve the life of a display panel In an emission display apparatus with a display panel in which a plurality of pixels each having at least one subpixel (11a, 11b, 11c) are disposed A first display method of emitting light with only a pixel P(I,j) serving as an emission center and a second display method of allocating luminance of the pixel P(i,j) serving as an emission center to nearby pixels surrounding the pixel are combined in a controllable manner A high-resolution mode with a high ratio of the first display method and a long-life mode with a high ratio of the second display method are switched therebetween depending on a spatial change or time change of image input data, an emission time, a degradation rate, a temperature, an emission luminance, and a display time

Evaluating Fourier Cross-Correlation Sub-Pixel Registration in Landsat Images

Abstract: Multi-temporal analysis is one of the main applications of remote sensing, and Landsat imagery has been one of the main resources for many years. However, the moderate spatial resolution (30 m) restricts their use for high precision applications. In this paper, we simulate Landsat scenes to evaluate, by means of an exhaustive number of tests, a subpixel registration process based on phase correlation and the upsampling of the Fourier transform. From a high resolution image (0.5 m), two sets of 121 synthetic images of fixed translations are created to simulate Landsat scenes (30 m). In this sense, the use of the point spread function (PSF) of the Landsat TM (Thematic Mapper) sensor in the downsampling process improves the results compared to those obtained by simple averaging. In the process of obtaining sub-pixel accuracy by upsampling the cross correlation matrix by a certain factor, the limit of improvement is achieved at 0.1 pixels. We show that image size affects the cross correlation results, but for images equal or larger than 100 × 100 pixels similar accuracies are expected. The large dataset used in the tests allows us to describe the intra-pixel distribution of the errors obtained in the registration process and how they follow a waveform instead of random/stochastic behavior. The amplitude of this waveform, representing the highest expected error, is estimated at 1.88 m. Finally, a validation test is performed over a set of sub-pixel shorelines obtained from actual Landsat-5 TM, Landsat-7 ETM+ (Enhanced Thematic Mapper Plus) and Landsat-8 OLI (Operation Land Imager) scenes. The evaluation of the shoreline accuracy with respect to permanent seawalls, before and after the registration, shows the importance of the registering process and serves as a non-synthetic validation test that reinforce previous results.

Accurate Dense Stereo Reconstruction using Graphics Hardware

Abstract: Vertex programs and pixel shaders found in modern graphics hardware are commonly used to enhance the realism of rendered scenes. Recently these hardware facilities were exploited to obtain interactive non-photorealistic effects and to perform low-level image processing tasks like texture filtering for volume visualization. We exploit modern graphics hardware to accomplish the higher level vision task of dense stereo reconstruction. In our system almost every stage of the matching procedure is executed on 3D graphics hardware, therefore utilizing the parallel vertex and pixel pipelines. Our implementation performs accurate calculations and does not suffer from the limited precision of color channels. On state of the art PC hardware our algorithm requires less than one second to reconstruct a dense mesh with subpixel accuracy for input images with one megapixel resolution.