Subpixel rendering

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

EXACT: algorithm and hardware architecture for an improved A-buffer

Abstract: Computer Graphics, vol 27, no 4, August 1993 (SIGGRAPH ’93 Proceedings), pp 85–92 The EXACT (EXact Area Coverage calculaTion) algorithm presented in this paper solves the Hidden Surface Elimination (HSE) problem on the subpixel level The use of subpixel masks for anti-aliasing causes some problems with the HSE on the pixel level that are difficult to overcome The approximations of the well known A-buffer algorithm are replaced by an exact solution that avoids erratic pixels along intersecting or touching surfaces With EXACT the HSE problem on the subpixel level is solved with the help of p-masks P-masks (priority masks) are subpixel masks that indicate for each subpixel which one of two given planes is closer to the viewer An algorithm to produce the p-masks in an efficient way and its hardware implementation are presented The p-mask generator is used in a hardware implementation of an A-buffer algorithm in the form of a rendering pipeline Of course the algorithm can also be used in software to enhance an existing A-buffer implementation The paper ends with the description of the list processing architecture for which the EXACT A-buffer has been built1 ∗Wilhelm–Schickard–Institut fur Informatik, Graphisch–Interaktive Systeme, Auf der Morgenstelle 10/C9, 7400 Tubingen, E-mail: andreas@grisinformatikuni-tuebingende, strasser@grisinformatikuni-tuebingende 1The experiences described here were gained in a research project partly supported by the Commission of the European Communities through the ESPRIT II-Project SPIRIT-workstation, Project No 2484 CR

Noise in flat-panel displays with subpixel structure.

Abstract: Subpixel structures found in medical monochrome active-matrix liquid crystal displays (AMLCDs) affect noise estimates measured with conventional methods. In this work, we discuss methods that identify sources of noise and permit the comparison of luminance noise estimates across technologies independent of pixel design and device technology. We used a three-million pixel AMLCD with a pixel structure consisting of three color stripes, each in a two-domain, in-plane switching mode. Images of uniform fields displayed on the AMLCD were acquired using a low-noise, high-resolution CCD camera. The camera noise and flat-field response were characterized using a uniform light source constructed for this purpose. We show results in terms of spatial luminance noise and noise power spectrum for high-resolution images and for the same images processed with a pixel-aligned aperture. We find that the pixel-aligned aperture eliminates almost all the noise found in the high-resolution images, suggesting that most of the luminance noise in AMLCDs comes from the subpixel structure and less-than-100% aperture ratio, rather than from interpixel variations.

Super-resolution from unregistered aliased images

Abstract: Aliasing in images is often considered as a nuisance. Artificial low frequency patterns and jagged edges appear when an image is sampled at a too low frequency. However, aliasing also conveys useful information about the high frequency content of the image, which is exploited in super-resolution applications. We use a set of input images of the same scene to extract such high frequency information and create a higher resolution aliasing-free image. Typically, there is a small shift or more complex motion between the different images, such that they contain slightly different information about the scene. Super-resolution image reconstruction can be formulated as a multichannel sampling problem with unknown offsets. This results in a set of equations that are linear in the unknown signal coefficients but nonlinear in the offsets. This thesis concentrates on the computation of these offsets, as they are an essential prerequisite for an accurate high resolution reconstruction. If a part of the image spectra is free of aliasing, the planar shift and rotation parameters can be computed using only this low frequency information. In such a case, the images can be registered pairwise to a reference image. Such a method is not applicable if the images are undersampled by a factor of two or larger. A higher number of images needs to be registered jointly. Two subspace methods are discussed for such highly aliased images. The first approach is based on a Fourier description of the aliased signals as a sum of overlapping parts of the spectrum. It uses a rank condition to find the correct offsets. The second one uses a more general expansion in an arbitrary Hilbert space to compute the signal offsets. The sampled signal is represented as a linear combination of sampled basis functions. The offsets are computed by projecting the signal onto varying subspaces. Under certain conditions, in particular for bandlimited signals, the nonlinear super-resolution equations can be written as a set of polynomial equations. Using Buchberger's algorithm, the solution can then be computed as a Grobner basis for the corresponding polynomial ideal. After a description of a standard algorithm, adaptations are made for the use with noisy measurements. The techniques presented in this thesis are tested in simulations and practical experiments. The experiments are performed on sets of real images taken with a digital camera. The results show the validity of the algorithms: registration parameters are computed with subpixel precision, and aliasing is accurately removed from the resulting high resolution image. This thesis is produced according to the concepts of reproducible research. All the results and examples used in this thesis are reproducible using the code and data available online.

Performance comparison of aperture codes for multimodal, multiplex spectroscopy

Abstract: We experimentally evaluate diverse static independent column codes in a coded aperture spectrometer. The performance of each code is evaluated based on the signal-to-noise ratio (SNR), defined as the peak value in the spectrum to the standard deviation of the background noise, as a function of subpixel vertical misalignments. Among the code families tested, an S-matrix-based code produces spectral reconstructions with the highest SNR. The SNR is least sensitive to vertical subpixel misalignments on the detector with a Hadamard-matrix-based code. Finally, the increased sensitivity of a spectrometer using a coded aperture instead of a slit is demonstrated.