Subpixel rendering

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

A Novel Method to Estimate Subpixel Temperature by Fusing Solar-Reflective and Thermal-Infrared Remote-Sensing Data With an Artificial Neural Network

Abstract: Among the multisource data fusing methods, the potential advantages of remote sensing of solar-reflective visible and near-Infrared [(VNIR); 400-900 nm] data and thermal-infrared (TIR) data have not been fully mined. Usually, a linear unmixed method is used for the purpose, which results in low estimation accuracy of subpixel land-surface temperature (LST). In this paper, we propose a novel method to estimate subpixel LST. This approach uses the characteristics of high spatial-resolution advanced spaceborne thermal emission and reflection radiometer (ASTER) VNIR data and the low spatial-resolution TIR data simulated from ASTER temperature product to generate the high spatial-resolution temperature data at a subpixel scale. First, the land-surface parameters (e.g., leaf area index, normalized difference vegetation index (NDVI), soil water content index, and reflectance) were extracted from VNIR data and field measurements. Then, the extracted high resolution of land-surface parameters and the LST were simulated into coarse resolutions. Second, the genetic algorithm and self-organizing feature map artificial neural network (ANN) was utilized to create relationships between land-surface parameters and the corresponding LSTs separately for different land-cover types at coarse spatial-resolution scales. Finally, the ANN-trained relationships were applied in the estimation of subpixel temperatures (at high spatial resolution) from high spatial-resolution land-surface parameters. The two sets of data with different spatial resolutions were simulated using an aggregate resampling algorithm. Experimental results indicate that the accuracy with our method to estimate land-surface subpixel temperature is significantly higher than that with a traditional method that uses the NDVI as an input parameter, and the average error of subpixel temperature is decreased by 2-3 K with our method. This method is a simple and convenient approach to estimate subpixel LST from high spatial-temporal resolution data quickly and effectively.

Hardware accelerated rendering of antialiasing using a modified a-buffer algorithm

Abstract: one pass per subpixel sample) through the hardware rendering pipeline. The resulting image is very high quality, but the performance degrades in proportion to the number of subpixel samples used by the filter function. This paper describes algorithms for accelerating antialiasing in 3D graphics through low-cost custom hardware. The rendering architecture employs a multiple-pass algorithm to perform front-to-back hidden surface removal and shading. Coverage mask evaluation is used to composite objects in 3D. The key advantage of this approach is that antialiasing requires no additional memory and decreases rendering performance by only 30-40% for typical images. The system is image partition based and is scalable to satisfy a wide range of performance and cost constraints. An A-buffer implementation does not require several passes of the object data, but does require sorting objects by depth before compositing them. The amount of memory required to store the sorted layers is limited to the number of subpixel samples, but it is significant since the color, opacity and mask data are needed for each layer. The compositing operation uses a blending function which is based on three possible subpixel coverage components and is more computationally intensive than the accumulation buffer blending function. The difficulty of implementing the A-buffer algorithm in hardware is described by Molnar [12]. CR

Macrodetector based image conversion system

Abstract: An image detection and pixel processing system includes a plurality of detector elements for receiving an image. The detector elements are subdivided into a plurality of macrodetectors, with each macrodetector constituting four or more detector elements, and with each macrodetector providing information for determining both a total light intensity value within the macrodetector and a centroid of light intensity indicative of light intensity position within the macrodetector. An image processing assembly receives information from the plurality of macrodetectors, with the image processing assembly relating a pixel and its encompassed subpixel area to each corresponding macrodetector, and further determining the total light intensity within the pixel and the centroid of light intensity within the subpixel. The image processing assembly is capable of rendering each subpixel area as an edge when magnitude of the centroid of light intensity is greater than a predetermined threshold.

Adaptive pixel-super-resolved lensfree in-line digital holography for wide-field on-chip microscopy.

Abstract: High-resolution wide field-of-view (FOV) microscopic imaging plays an essential role in various fields of biomedicine, engineering, and physical sciences. As an alternative to conventional lens-based scanning techniques, lensfree holography provides a new way to effectively bypass the intrinsical trade-off between the spatial resolution and FOV of conventional microscopes. Unfortunately, due to the limited sensor pixel-size, unpredictable disturbance during image acquisition, and sub-optimum solution to the phase retrieval problem, typical lensfree microscopes only produce compromised imaging quality in terms of lateral resolution and signal-to-noise ratio (SNR). Here, we propose an adaptive pixel-super-resolved lensfree imaging (APLI) method which can solve, or at least partially alleviate these limitations. Our approach addresses the pixel aliasing problem by Z-scanning only, without resorting to subpixel shifting or beam-angle manipulation. Automatic positional error correction algorithm and adaptive relaxation strategy are introduced to enhance the robustness and SNR of reconstruction significantly. Based on APLI, we perform full-FOV reconstruction of a USAF resolution target (~29.85 mm2) and achieve half-pitch lateral resolution of 770 nm, surpassing 2.17 times of the theoretical Nyquist–Shannon sampling resolution limit imposed by the sensor pixel-size (1.67µm). Full-FOV imaging result of a typical dicot root is also provided to demonstrate its promising potential applications in biologic imaging.