Subpixel rendering

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

Robust Fine Registration of Multisensor Remote Sensing Images Based on Enhanced Subpixel Phase Correlation.

Abstract: Automatic fine registration of multisensor images plays an essential role in many remote sensing applications. However, it is always a challenging task due to significant radiometric and textural differences. In this paper, an enhanced subpixel phase correlation method is proposed, which embeds phase congruency-based structural representation, L1-norm-based rank-one matrix approximation with adaptive masking, and stable robust model fitting into the conventional calculation framework in the frequency domain. The aim is to improve the accuracy and robustness of subpixel translation estimation in practical cases. In addition, template matching using the enhanced subpixel phase correlation is integrated to realize reliable fine registration, which is able to extract a sufficient number of well-distributed and high-accuracy tie points and reduce the local misalignment for coarsely coregistered multisensor remote sensing images. Experiments undertaken with images from different satellites and sensors were carried out in two parts: tie point matching and fine registration. The results of qualitative analysis and quantitative comparison with the state-of-the-art area-based and feature-based matching methods demonstrate the effectiveness and reliability of the proposed method for multisensor matching and registration.

Efficient deep learning for stereo matching with larger image patches

Abstract: Stereo matching plays an important role in many applications, such as Advanced Driver Assistance Systems, 3D reconstruction, navigation, etc. However it is still an open problem with many difficult. Most difficult are often occlusions, object boundaries, and low or repetitive textures. In this paper, we propose a method for processing the stereo matching problem. We propose an efficient convolutional neural network to measure how likely the two patches matched or not and use the similarity as their stereo matching cost. Then the cost is refined by stereo methods, such as semiglobal maching, subpixel interpolation, median filter, etc. Our architecture uses large image patches which makes the results more robust to texture-less or repetitive textures areas. We experiment our approach on the KITTI2015 dataset which obtain an error rate of 4.42% and only needs 0.8 second for each image pairs.

Map-based road detection in spaceborne synthetic aperture radar images based on curvilinear structure extraction

Abstract: This paper presents an automatic map-based road detection algorithm for spaceborne synthetic aperture radar (SAR) images. Our goal is to find roads in a SAR image with subpixel accuracy with the help of a digital map. There are location errors between the digital map and the geocoded SAR image, which are about 20 to 30 pixels, and we adopt a coarse-to-fine strategy to reduce it. In the coarse matching step, we roughly find the locations of roads by a simple search using water areas or a generalized Hough transform based on digital map information. The fine matching step detects roads accurately by using the active contour model (snake). The input of the snake operation is the potential field constructed from the extracted ridges or ravines of curvilinear structures in the SAR image. Experimental results show that our algorithm detects roads with average error of less than one pixel. © 2000 Society of Photo- Optical Instrumentation Engineers. (S0091-3286(00)01309-X)

Compensated drive signal for electroluminescent display

Abstract: Compensation is performed for initial nonuniformity or aging of drive transistors and electroluminescent (EL) emitters in 3T1C EL subpixels of an EL display, such as an organic light-emitting diode (OLED) display. A readout transistor connected to the EL emitter is used to readout the voltage of the emitter and compensation for ΔV th , ΔV EL , and OLED efficiency loss is performed using a model. Measurements are taken during a frame by driving a target subpixel at a higher luminance for a shorter time, then using the remaining time in the frame to measure. Measurements can be taken with an A/D converter or with a ramp generator and comparator. Compensation is performed for each subpixel individually.