This paper introduces a general-purpose quasi-cyclic (QC)-low-density parity-check (LDPC) processor (GPLP) developed to evaluate LDPC decoding performance, which is a highly flexible and programmable field programmable gate array (FPGA)-based simulation platform. The GPLP enables to configure LDPC decoding processes, depending on the decoding algorithms and design choices to be simulated and verified. In contrast to traditional FPGA-based approaches, which typically rely on a fixed dataflow, the GPLP offers a reconfigurable, processor-like environment that can be programmed for various design configurations. This paper defines essential atomic operations required for QC-LDPC decoding and proposes the GPLP architecture supporting those operations with vector-based processing. The proposed GPLP significantly enhances simulation speed, providing a performance improvement of 220x to 574x over CPU-based simulations. The platform provides a unique combination of simulation speed, flexibility, and reconfigurability, making it an invaluable tool to rapidly evaluate and optimize LDPC decoding performance.

This paper proposes a hardware-efficient multiple-resolution decoding architecture for quasi-cyclic low-density parity-check (QC-LDPC) codes, specifically designed to meet the stringent requirements of the 5G New Radio (NR) standard. The proposed architecture adopts a single-instruction multiple-data (SIMD) approach to dynamically adjust the bitwidth of log-likelihood ratio (LLR) values based on theEb/N0condition, significantly reducing hardware complexity and improving throughput area ratio. Unlike conventional single-resolution decoders, the architecture processes 2-bit LLR values in highEb/N0regions and scales up to 4-bit or 8-bit LLR values for moderate and lowEb/N0conditions, maintaining robust error-correcting performance. Key innovations include SIMD-based design for variable-node units (VNUs), check-node units (CNUs), and quasi-cyclic shifting networks (QSNs), as well as optimized memory access scheduling to support all 51 lifting sizes defined in the 5G NR standard. Designed in a 65-nm CMOS process, the decoder achieves a peak throughput of 27.24 Gbps under error-free conditions with a throughput area ratio improvement of 2.07× compared to the state-of-the-art designs. Furthermore, the proposed architecture demonstrates superior throughput-area ratio and flexibility, supporting all code rates and lifting sizes specified in the 5G NR standard. Simulation results confirm that the proposed decoder meets the peak throughput requirement under error-free conditions, while maintaining robust performance in challenging channel environments.

Digital cameras with electronic viewfinders provide a relatively faithful depiction of the final image, providing a WYSIWYG experience. If, however, the image is created from a burst of differently captured images, or non-linear interactive edits significantly alter the final outcome, then the photographer cannot directly see the results, but instead must imagine the post-processing effects. This paper explores the notion of viewfinder editing , which makes the viewfinder more accurately reflect the final image the user intends to create. We allow the user to alter the local or global appearance (tone, color, saturation, or focus) via stroke-based input, and propagate the edits spatiotemporally. The system then delivers a real-time visualization of these modifications to the user, and drives the camera control routines to select better capture parameters.

We present a flexible method for fusing information from optical and range sensors based on an accelerated high-dimensional filtering approach. Our system takes as input a sequence of monocular camera images as well as a stream of sparse range measurements as obtained from a laser or other sensor system. In contrast with existing approaches, we do not assume that the depth and color data streams have the same data rates or that the observed scene is fully static. Our method produces a dense, high-resolution depth map of the scene, automatically generating confidence values for every interpolated depth point. We describe how to integrate priors on object motion and appearance and how to achieve an efficient implementation using parallel processing hardware such as GPUs.

Abstract Many useful algorithms for processing images and geometry fall under the general framework of high‐dimensional Gaussian filtering. This family of algorithms includes bilateral filtering and non‐local means. We propose a new way to perform such filters using the permutohedral lattice, which tessellates high‐dimensional space with uniform simplices. Our algorithm is the first implementation of a high‐dimensional Gaussian filter that is both linear in input size and polynomial in dimensionality. Furthermore it is parameter‐free, apart from the filter size, and achieves a consistently high accuracy relative to ground truth (> 45 dB). We use this to demonstrate a number of interactive‐rate applications of filters in as high as eight dimensions.

Although there has been much interest in computational photography within the research and photography communities, progress has been hampered by the lack of a portable, programmable camera with sufficient image quality and computing power. To address this problem, we have designed and implemented an open architecture and API for such cameras: the Frankencamera. It consists of a base hardware specification, a software stack based on Linux, and an API for C++. Our architecture permits control and synchronization of the sensor and image processing pipeline at the microsecond time scale, as well as the ability to incorporate and synchronize external hardware like lenses and flashes. This paper specifies our architecture and API, and it describes two reference implementations we have built. Using these implementations we demonstrate six computational photography applications: HDR viewfinding and capture, low-light viewfinding and capture, automated acquisition of extended dynamic range panoramas, foveal imaging, IMU-based hand shake detection, and rephotography. Our goal is to standardize the architecture and distribute Frankencameras to researchers and students, as a step towards creating a community of photographer-programmers who develop algorithms, applications, and hardware for computational cameras.

High-dimensional Gaussian filters, most notably the bilateral filter, are important tools for many computer graphics and vision tasks. In recent years, a number of techniques for accelerating their evaluation have been developed by exploiting the separability of these Gaussians. However, these techniques do not apply to the more general class of spatially varying Gaussian filters, as they cannot be expressed as convolutions. These filters are useful because the underlying data---e.g. images, range data, meshes or light fields---often exhibit strong local anisotropy and scale. We propose an acceleration method for approximating spatially varying Gaussian filters using a set of spatially invariant Gaussian filters each of which is applied to a segment of some non-disjoint partitioning of the dataset. We then demonstrate that the resulting ability to locally tilt, rotate or scale the kernel improves filtering performance in various applications over traditional spatially invariant Gaussian filters, without incurring a significant penalty in computational expense.