Look-up Table Research Articles

Clustered OLSR routing and FPGA parameter analysis for mobile ad hoc communication

ABSTRACT The research letter emphasises the hardware chip design of homogeneous clustered optimised link state routing (HMC-OLSR) protocol for Mobile Ad-hoc Networks (MANET). The homogeneously distributed clustering allows for parallel processing and distributes the burden evenly among the clusters to reduce the network complexity and computational time. The chip design is evaluated based on field programmable gate array (FPGA) performance metrics for the proposed HMC-OLSR protocol, which significantly reduced hardware utilisation by 27.45%, 48.8% of slices, 28.71%, 55.82% of flip-flops, 8.35%, 13.60% of look-up tables (LUTs), and 36.4% of input/output blocks (IoBs) compared to existing OLSR and destination-Sequenced distance vector (DSDV) protocols, respectively. The recommended protocol also outperformed OLSR and DSDV routing protocols in FPGA timing constraints such as minimum time before the clock, maximum time after the clock, and minimum propagation period. The novelty of the work is in the incorporation of an FPGA-integrated scalable network design that surpasses maximum throughput, packet delivery ratio (PDR), minimum power consumption, end-to-end delay, and control overhead.

A human metabolic map of pharmacological perturbations reveals drug modes of action.

Understanding a small molecule's mode of action (MoA) is essential to guide the selection, optimization and clinical development of lead compounds. In this study, we used high-throughput non-targeted metabolomics to profile changes in 2,269 putative metabolites induced by 1,520 drugs in A549 lung cancer cells. Although only 26% of the drugs inhibited cell growth, 86% caused intracellular metabolic changes, which were largely conserved in two additional cancer cell lines. By testing more than 3.4 million drug-metabolite dependencies, we generated a lookup table of drug interference with metabolism, enabling high-throughput characterization of compounds across drug therapeutic classes in a single-pass screen. The identified metabolic changes revealed previously unknown effects of drugs, expanding their MoA annotations and potential therapeutic applications. We confirmed metabolome-based predictions for four new glucocorticoid receptor agonists, two unconventional 3-hydroxy-3-methylglutaryl-CoA (HMGCR) inhibitors and two dihydroorotate dehydrogenase (DHODH) inhibitors. Furthermore, we demonstrated that metabolome profiling complements other phenotypic and molecular profiling technologies, opening opportunities to increase the efficiency, scale and accuracy of preclinical drug discovery.

Theoretical Proof and Implementation of Digital Beam Control and Beamforming Algorithm for Low Earth Orbit Satellite Broadcast Signal Reception Processing Terminal

Compared to analog beamforming, digital beamforming offers better self-calibration and lower sidelobe performance, which has a profound impact on improving low Earth orbit receiver performance. The Digital Beamforming (DBF) module in the low Earth orbit satellite broadcast signal reception terminal can use digital phase shifting to compensate for the phase differences caused by path and spatial distance variations due to inconsistent Radio Frequency (RF) channel delays. This compensation ensures in-phase summation, thereby achieving maximum energy reception in the desired direction. Although DBF has gained widespread attention in the radar field due to its unique functions and advantages, its application is limited by beamforming accuracy and gain. Therefore, with the development of DBF technology, how to improve its accuracy and gain has also attracted extensive attention both domestically and internationally. To address this issue, this paper proposes a beamforming method based on a cap-shaped array for low Earth orbit satellite broadcast signal reception and processing terminals. The method combines prior information and spatial domain search for beam control, and employs a lookup table for beam synthesis. It derives formulas for the Signal-to-Noise Ratio, noise figure, processing flow of the beamforming network, and the determination of beamforming weights for the spherical antenna array. The paper presents a beam control approach that combines prior information with spatial domain search, along with an implementation process for beam synthesis using a lookup table. It also details the corresponding Field-Programmable Gate Array (FPGA) implementation process. Finally, the beamforming algorithm is experimentally validated, and error analysis is conducted. The experimental results show that the measured beamforming sensitivity at all incident angles is below −133 dBm and the G/T values are all greater than −9 dB/K, the beam uniformity at three operating frequencies is less than 3°, and the measured errors in pitch and azimuth angles are both below 2°. The beam pointing error is also below 2°.

A Comparative Analysis of Lithium-Ion Batteries Using a Proposed Electrothermal Model Based on Numerical Simulation

It is necessary to maintain safe, efficient, and compatible energy storage systems to meet the high demand for electric vehicles (EVs). Lithium manganese nickel cobalt (NMC) and lithium ferro phosphate (LFP) batteries are the most commonly used lithium batteries in EVs. It is imperative to note that batteries are classified according to their electrochemical performance. A number of factors play a crucial role in determining how efficiently batteries can be used. These factors include the cell temperature, energy density, self-discharge, current limits, aging, and performance measurements. This paper offers a proposed electrothermal model for comparison between LFP and NMC batteries. This model demonstrates the different behaviors according to their application in EVs. This is carried out through studies of state of charge (SoC), state of health (SoH), thermal runaway, self-discharge, and remaining useful life (RUL) in EVs. According to numerical analysis, this paper examines how these different types of batteries behave in EVs to assist in the selection of the most suitable battery taking into account the operating temperature and discharge current using a helpful thermoelectric model reflecting battery safety and life span effectively. Using MATLAB Simulink, the data selected in the electrothermal model are combined from a number of references that are incorporated into lookup tables that affect the change in values in the electrothermal model. The cells are implemented in an EV system using a current test to examine the measured current that goes in and comes out of the battery cells during charging and discharging processes taking into account motoring and regenerative braking for a specified drive cycle time and a number of discharging cycles. It was found that LFP batteries have better stability for open circuit voltages of 3.34 volts over a wide range of conducted temperatures. NMC batteries, on the other hand, exhibit some open circuit voltage variation of 0.053 volts over the temperature range used. Furthermore, the self-discharging current of LFP batteries was about 12 times lower than that of NMC batteries. Compared to LFP batteries, NMC batteries have a higher energy density per unit of mass of 150%, which reflects their greater discharge range. As a result of temperature effects, it has been revealed that LFP batteries are about two times more stable during discharging than NMC batteries, particularly at higher temperatures, such as 45 degrees.

Rule4ml: an open-source tool for resource utilization and latency estimation for ML models on FPGA

Abstract Implementing Machine Learning (ML) models on Field-Programmable Gate Arrays (FPGAs) is becoming increasingly popular across various domains as a low-latency and low-power solution that helps manage large data rates generated by continuously improving detectors. However, developing ML models for FPGAs is time-consuming, as optimization requires synthesis to evaluate FPGA area and latency, making the process slow and repetitive. This paper introduces a novel method to predict the resource utilization and inference latency of Neural Networks (NNs) before their synthesis and implementation on FPGA. We leverage HLS4ML, a tool-flow that helps translate NNs into high-level synthesis (HLS) code, to synthesize a diverse dataset of NN architectures and train resource utilization and inference latency predictors. While HLS4ML requires full synthesis to obtain resource and latency insights, our method uses trained regression models for immediate pre-synthesis predictions. The prediction models estimate the usage of Block RAM (BRAM), Digital Signal Processors (DSP), Flip-Flops (FF), and Look-Up Tables (LUT), as well as the inference clock cycles. The predictors were evaluated on both synthetic and existing benchmark architectures and demonstrated high accuracy with R2 scores ranging between 0.8 and 0.98 on the validation set and sMAPE values between 10% and 30%. Overall, our approach provides valuable preliminary insights, enabling users to quickly assess the feasibility and efficiency of NNs on FPGAs, accelerating the development and deployment processes. The open-source repository can be found at https://github.com/IMPETUS-UdeS/rule4ml, while the datasets are publicly available at https://borealisdata.ca/dataverse/rule4ml.

Utilizing Raspberry-Pi 3 to Implement Fuzzy Logic Controller Optimized by Genetic Algorithm

The development of Fuzzy Logic Controllers (FLC) with low error rates and cost effectiveness has been the subject of numerous studies. This paper study goals to the investigation and then implementation an FLC using the readily accessible and reasonably priced Raspberry Pi technology. The FLC used in this work has two inputs, one output, and five Membership Functions (MFs) for each input and output. The FLC goes through two processes, tweaking the MF parameters and tuning input/ output Scaling Factors. The tuning technique makes use of the Genetic Algorithm (GA). The whole set of the FLC probabilities is taken into account as the tuned FLC controller, and then transformed into a lookup table. The Center of Gravity (COG) approach is used to determine the output for the tuned FLC controller. The resulting table is converted into values of digital binary using a specific type of encoder, and then extraction of the set of Boolean functions to apply this tuned circuit. Finally, the Python 3 programming language is used to define the resultant Boolean functions on the Raspberry Pi platform, and then a decoder extracted the appropriate control action from the output. The Benefit of this method is the use of a digital numbering system to define the FLC, which is implemented on Raspberry Pi technology and allows for fuzzified high processing speed output per second. The controller speed has not been unaffected by the quantity for these fuzzy rules.

Crystal-level timing calibration using cascaded photons of60Co point source for long axial animal PET system.

Timing calibration is essential for positron emission tomography (PET) system as it enhances timing resolution to improve image quality. Traditionally, positron sources are employed for timing calibration. However, the photons emitted by these sources travel in opposite directions, necessitating that positrons annihilate at multiple locations to collect coincidence data across a greater number of lines of response (LORs). To overcome this limitation, this study proposes a timing calibration method utilising a60Co point source. The60Co source emits cascaded photons without angular correlation, allowing the collection of coincidence events throughout the field of view (FOV) with a single60Co point source positioned at the centre of the FOV to determine the timing offsets of the pixels. Leveraging the properties of60Co, we propose a calibration method and implement it on a long axial animal PET system. Initially, we calibrated the timing offsets of the pixels within two blocks to establish reference detectors, and subsequently employed a60Co point source to determine the timing offsets of all the pixels in the system relative to these reference detectors. In addition, we evaluated the system's timing resolution before and after the calibration to validate the efficacy of the proposed method. We measured the timing offsets of the pixels across the entire system, ranging from -5.0 to 2.0 ns. After implementing the timing offset lookup table, the system timing resolution was improved from 6.30 ns before calibration to 1.04 ns. In this study, the60Co source is employed for timing calibration, offering the advantages of operational simplicity, broad applicability, and potential application in time-of-flight (TOF) PET.

Mapping neutron biological effectiveness for DNA damage induction as a function of incident energy and depth in a human sized phantom

We present new developments for an ab-initio model of the neutron relative biological effectiveness (RBE) in inducing specific classes of DNA damage. RBE is evaluated as a function of the incident neutron energy and of the depth inside a human-sized reference spherical phantom. The adopted mechanistic approach traces neutron RBE back to its origin, i.e. neutron physical interactions with biological tissues. To this aim, we combined the simulation of radiation transport through biological matter, performed with the Monte Carlo code PHITS, and the prediction of DNA damage using analytical formulas, which ground on a large database of biophysical radiation track structure simulations performed with the code PARTRAC. In particular, two classes of DNA damage were considered: sites and clusters of double-strand breaks (DSBs), which are known to be correlated with cell fate following radiation exposure. Within a coherent modelling framework, this approach tackles the variation of neutron RBE in a wide energy range, from thermal neutrons to neutrons of hundreds of GeV, and reproduces effects related to depth in the human-sized receptor, as well as to the receptor size itself. Besides providing a better mechanistic understanding of neutron biological effectiveness, the new model can support better-informed decisions for radiation protection: indeed, current neutron weighting (ICRP)/quality (U.S. NRC) factors might be insufficient for use in some radiation protection applications, because they do not account for depth. RBE predictions obtained with the reported model were successfully compared to the currently adopted radiation protection standards when the depth information is not relevant (at the shallowest depth in the phantom or for very high energy neutrons). However, our results demonstrate that great care is needed when applying weighting factors as a function of incident neutron energy only, not explicitly considering RBE variation in the target. Finally, to facilitate the use of our results, we propose look-up RBE tables, explicitly considering the depth variable, and an analytical representation of the maximal RBE vs. neutron energy.

Wakeup-Darkness: When Multimodal Meets Unsupervised Low-light Image Enhancement

Low-light image enhancement is a crucial visual task, and many unsupervised methods overlook the degradation of visible information in low-light scenes, adversely affecting the fusion of complementary information and hindering the generation of satisfactory results. To address this, we introduce Wakeup-Darkness, a multimodal enhancement framework that innovatively enriches user interaction through voice and textual commands. This approach signifies a technical leap and represents a paradigm shift in user engagement. We introduce a cross-modal feature fusion (CMFF) that synergizes semantic and depth context with low-light enhancement operations. Moreover, We propose a gated residual block (GRB) and a channel-aware look-up table (LUT) to adjust the intensity distribution of each channel. Crucially, the proposed Wakeup-Darkness scheme demonstrates remarkable generalization in unsupervised scenarios. The source code can be accessed from https://github.com/zhangbaijin/Wakeup-Dakness .

Diwall: A Lightweight Host Intrusion Detection System Against Jamming and Packet Injection Attacks

The rapid growth of Internet of Things (IoTs) applications in various sectors has led to a significant increase in the number of IoT devices. This has led to the deployment of numerous IoT protocols to provide greater connectivity. However, this extensive adoption has also left them vulnerable to attack. In particular, attacks targeting wireless communication capabilities represent a significant threat. Such attacks exploit various vulnerabilities in the wireless connectivity unit, compromising its security. To counter this threat, this paper proposes a Host Intrusion Detection System (HIDS) for detecting wireless attacks. Its components are customized to support IoT end-devices using low-GHz and sub-GHz data rate protocols. The HIDS deploys a hardware tracer to monitor microarchitecture and network metrics using hardware performance counters (HPCs). It performs monitoring of network and microarchitecture metrics for a 32-bit RISC-V based wireless connectivity unit. The HIDS uses analysis and classification of monitored data for detecting memory corruption and jamming attacks. We evaluate the effectiveness of the HIDS in detecting packet injection and jamming attacks. Our FPGA implementation of HIDS has a logic overhead of about \(14.30\% \) and \(22.89\% \) of flip flops (FFs) and lookup tables (LUTs), respectively, compared to the CV32E40P baseline on an Arty A7 100T board. The design frequency and code size penalties are less than \(1\% \) for a RISC-V processor with a LoRaWAN protocol stack.

Mitigating Randomness Leakage in SM2 White-Box Implementations via Trusted Execution Environments

White-box cryptography plays a vital role in untrusted environments where attackers can fully access the execution process and potentially expose cryptographic keys. It secures keys by embedding them within complex and obfuscated transformations, such as lookup tables and algebraic manipulations. However, existing white-box protection schemes for SM2 signatures face vulnerabilities, notably random number leakage, which compromises key security and diminishes overall effectiveness. This paper proposes an improved white-box implementation of the SM2 signature computation leveraging a Trusted Execution Environment (TEE) architecture. The scheme employs three substitution tables for SM2 key generation and signature processes, orchestrated by a random bit string k. The k value and lookup operations are securely isolated within the TEE, effectively mitigating the risk of k leakage and enhancing overall security. Experimental results show our scheme enhances security, reduces storage, and improves performance over standard SM2 signature processing, validating its efficacy with TEE and substitution tables in untrusted environments.

New gantry angle-dependent beam control optimization with Elekta linear accelerator for VMAT delivery.

This paper describes a method to improve gantry-dependent beam steering for Elekta traveling wave linear accelerators by applying the measured and filtered beam servo corrections to the existing lookup table (LUT). Beam steering has a direct influence on the treatment accuracy by affecting the beam symmetry and position. The presented method provides an improved LUT with respect to the default Elekta method to reduce treatment delivery interruptions. These interruptions are known to contribute to unwanted intrafraction motion and longer treatment times. Compared to the default method of the manufacturer, this new method takes both clockwise and counterclockwise rotation to compensate for magnetic hysteresis as well as previous configuration and noise filtering into account. The improved method to determine the lookup table uses service graphing information from the linac without the need for additional symmetry information. The clinical configuration of the flattened beam energies remains untouched during the data record. This method results in a configuration where the gantry-dependent steering is optimized over the full arc with optimal balance in the hysteresis and minimizing the effect of errors in the steering values. This method is a less error-prone process compared to the methodology described in previous research, still achieving a reduction of interruption of about 60 percent compared to the Elekta method. This study shows a simplified way to optimize linac stability with improved LUT. The optimized LUT results in a lower number of interruptions, preventing downtime, and a lower risk of intrafraction motion due to longer treatment time.

AdvLUT: Cloaking Geographic Location With Semantic-Based Adversarial 3-D Lookup Tables

AdvLUT: Cloaking Geographic Location With Semantic-Based Adversarial 3-D Lookup Tables

Quantum Machine Learning for Video Compression: An Optimal Video Frames Compression Model using Qutrits Quantum Genetic Algorithm for Video multicast over the Internet

The transmission of video is greatly aided by video compression. Redundancy (spatial, temporal, statistical, and psycho-visual) within and between video frames is something that video compression approaches aim to get rid of. The degree to which similarity-based redundancy exists between consecutive frames, however, is a function of how often the frames are sampled and how the objects in the scene are moving. Existing neural network-based video compression approaches rely on a static codebook to perform compression, which prevents them from adapting to new video’s data. In order to create an optimal codebook for vector quantization, which is then employed as an activation function inside a neural network's hidden layer, this research offers a modified video compression method based on a Qutrits based Quantum Genetic Algorithm (QQGA). Using quantum parallelization and entanglement of the quantum state, QQGA is capable of solving the same set of problems as a traditional genetic algorithm while considerably accelerating the evolutionary process. The technique is built on the concept of utilizing Qutrits (three-level quantum system) to represent population individuals. The evolution operator, which is responsible for the updates to the quantum system state, has been constructed using a straightforward approach that does not need a lookup table. Compared to qubit, qudit provides a larger state space to store and process information, and thus can enhance the algorithm’s efficiency. To create the context-based initial codebook, the background subtraction algorithm is used to extract moving objects from frames. Moreover, important wavelet coefficients are compressed losslessly using Differential Pulse Code Modulation (DPCM), whereas low energy coefficients are compressed lossy using Learning Vector Quantization neural networks (LVQ). To obtain a high compression ratio, Run-Length Encoding is then used to encode the quantized coefficients. In comparison to the conventional evolutionary algorithm-based video compression method, experiments have shown that the quantum-inspired system may achieve a greater compression ratio with acceptable efficiency as evaluated by PSNR.

The Brief International Cognitive Assessment in Multiple Sclerosis (BICAMS): Regression-based norms for German-speaking countries.

Cognitive impairment in multiple sclerosis (MS) is common and is associated with problems in employment, driving ability, and quality of life. Since cognitive impairment at time of diagnosis is predictive of disability progression, early assessment and annual monitoring is recommended. The Brief International Cognitive Assessment in Multiple Sclerosis (BICAMS) was introduced as a time-efficient screening tool that can easily be applied in standard clinical care. However, besides application, tests need to be analysed and the raw values obtained must be interpreted accordingly. Since normative values have not been available for German-speaking countries, BICAMS has not been implemented in clinical routine. The aim of this study was to calculate German normative BICAMS data to improve the current diagnostic process for people with MS. Healthy control subjects in different age categories were recruited to enable regression-based norm analysis. Raw scores for each BICAMS test were converted into scaled scores before they were regressed for age, age squared, gender, and education. The obtained scores were then normalised to a z-score by dividing the difference between the scaled score and the predicted score by the root mean squared error of the model. In all, 237 HCs were recruited (68.8% female, 31.2% male) and examined with BICAMS. Datasets entered the regression-based norms analysis and formed the basis for a final z-score calculation. To simplify handling in everyday clinical practice, nomograms and look-up tables were created which provide clinicians with age- and education-corrected norms. Our work fills the gaps between BICAMS application, evaluation and interpretation and will make a significant contribution to more regular cognitive assessment of MS patients.

Coupling SAR and optical remote sensing data for soil moisture retrieval over dense vegetation covered areas.

Soil moisture is a key parameter for the exchange of substance and energy at the land-air interface, timely and accurate acquisition of soil moisture is of great significance for drought monitoring, water resource management, and crop yield estimation. Synthetic aperture radar (SAR) is sensitive to soil moisture, but the effects of vegetation on SAR signals poses challenges for soil moisture retrieval in areas covered with vegetation. In this study, based on Sentinel-1 SAR and Sentinel-2 optical remote sensing data, a coupling approach was employed to retrieval surface soil moisture over dense vegetated areas. Different vegetation indices were extracted from Sentinel-2 data to establish the vegetation water content (VWC) estimation model, which was integrated with the Water Cloud Model (WCM) to distinguish the contribution of vegetation layer and soil layer to SAR backscattering signals. Subsequently, the Oh model and the Look-Up Table (LUT) algorithm were used for soil moisture retrieval, and the accuracy of the result was compared with the traditional direct retrieval method. The results indicate that, for densely vegetated surfaces, VWC can be better reflected by multiple vegetation indices including NDVI, NDWI2, NDGI and FVI, the R2 and RMSE of VWC estimation result is 0.709 and 0.30 kg·m-2. After vegetation correction, the correlation coefficient increased from 0.659 to 0.802 for the VV polarization, and from 0.398 to 0.509 for the VH polarization. Satisfactory accuracy of soil moisture retrieval result was obtained with the Oh model and the LUT algorithm, VV polarization is found to be more suitable for soil moisture retrieval compared to VH polarization, with an R2 of 0.672 and an RMSE of 0.048m3·m-3, the accuracy is higher than that of the direct retrieval method. The results of the study preliminarily verified the feasibility of the coupling method in soil moisture retrieval over densely veg etated surfaces.

Shade watch: Mapping citywide shade dynamics through ray tracing and LiDAR data in Hong Kong's complex 3-D built environment

The importance of urban shade in city planning has been underscored by its various associations with thermal comfort, living environment quality, human health and well-being. However, existing methods for urban shade mapping are often limited to street-level scopes or singular sampling observational times, leaving citywide shade mapping, its spatiotemporal patterns, and controlling factors largely unexplored. To fill these knowledge gaps, this study proposed a novel protocol that enables seamless mapping of spatiotemporal urban shade patterns over the entire city by fusing look-up-table (LUT)-based ray-tracing approach with Google Earth Engine cloud computing technology, as well as high-resolution digital surface model (DSM) derived from LiDAR data. This protocol was applied to Hong Kong, a city characterized by a complex 3-D built environment, and then delved into the spatiotemporal patterns of urban shade and the associated controlling factors. Validation results indicate that the proposed method can accurately quantify urban shade compared to ground-based photo references and high-resolution satellite imagery. The analysis uncovers that: (1) Shade exhibits spatial heterogeneity across different 3-D built environments while maintaining a consistent "U-shape" diurnal shade fraction during the day; (2) Solar geometry, regardless of solar zenith and azimuth angle, is positively associated with aggregated metrics of shade landscape and negatively associated with fragmented metrics. Moreover, solar zenith angles exert stronger controls over the citywide shade landscape patterns; and (3) Persistent shade significantly reduces the sunlight hours (i.e., the accumulated sunshine hours per day) across administrative districts, local climate zones (LCZs), and seasons. Due to shade effects originating from 3-D urban structures, Hong Kong typically experiences an average of 4-8 sunlight hours per day within a one-year cycle, which is substantially lower than the 11-14 hours of natural daylength. This study offers a practical protocol for large-scale urban shade mapping, enhancing our understanding of urban shade, its spatiotemporal dynamics, and benefits in a broader spatiotemporal context. The associated datasets and findings from this study can inform urban planners and policymakers in developing effective strategies to create healthier and sustainable urban environments in Hong Kong.

Data-driven parametrization of molecular mechanics force fields for expansive chemical space coverage.

A force field is a critical component in molecular dynamics simulations for computational drug discovery. It must achieve high accuracy within the constraints of molecular mechanics' (MM) limited functional forms, which offers high computational efficiency. With the rapid expansion of synthetically accessible chemical space, traditional look-up table approaches face significant challenges. In this study, we address this issue using a modern data-driven approach, developing ByteFF, an Amber-compatible force field for drug-like molecules. To create ByteFF, we generated an expansive and highly diverse molecular dataset at the B3LYP-D3(BJ)/DZVP level of theory. This dataset includes 2.4 million optimized molecular fragment geometries with analytical Hessian matrices, along with 3.2 million torsion profiles. We then trained an edge-augmented, symmetry-preserving molecular graph neural network (GNN) on this dataset, employing a carefully optimized training strategy. Our model predicts all bonded and non-bonded MM force field parameters for drug-like molecules simultaneously across a broad chemical space. ByteFF demonstrates state-of-the-art performance on various benchmark datasets, excelling in predicting relaxed geometries, torsional energy profiles, and conformational energies and forces. Its exceptional accuracy and expansive chemical space coverage make ByteFF a valuable tool for multiple stages of computational drug discovery.

A hybrid Mamba and sparse Look-Up Table network for perceptual-friendly underwater image enhancement

A hybrid Mamba and sparse Look-Up Table network for perceptual-friendly underwater image enhancement

Centroiding undersampled point spread functions with a lookup table

Centroiding undersampled point spread functions with a lookup table