Low Resolution Research Articles

Transformer Based Pluralistic Image Completion With Reduced Information Loss.

Transformer based methods have achieved great success in image inpainting recently. However, we find that these solutions regard each pixel as a token, thus suffering from an information loss issue from two aspects: 1) They downsample the input image into much lower resolutions for efficiency consideration. 2) They quantize 2563 RGB values to a small number (such as 512) of quantized color values. The indices of quantized pixels are used as tokens for the inputs and prediction targets of the transformer. To mitigate these issues, we propose a new transformer based framework called "PUT". Specifically, to avoid input downsampling while maintaining computation efficiency, we design a patch-based auto-encoder P-VQVAE. The encoder converts the masked image into non-overlapped patch tokens and the decoder recovers the masked regions from the inpainted tokens while keeping the unmasked regions unchanged. To eliminate the information loss caused by input quantization, an Un-quantized Transformer is applied. It directly takes features from the P-VQVAE encoder as input without any quantization and only regards the quantized tokens as prediction targets. Furthermore, to make the inpainting process more controllable, we introduce semantic and structural conditions as extra guidance. Extensive experiments show that our method greatly outperforms existing transformer based methods on image fidelity and achieves much higher diversity and better fidelity than state-of-the-art pluralistic inpainting methods on complex large-scale datasets (e.g., ImageNet).

Low-cost and convenient screening of disease using analysis of physical measurements and recordings

In recent years, there has been substantial work in low-cost medical diagnostics based on the physical manifestations of disease. This is due to advancements in data analysis techniques and classification algorithms and the increased availability of computing power through smart devices. Smartphones and their ability to interface with simple sensors such as inertial measurement units (IMUs), microphones, piezoelectric sensors, etc., or with convenient attachments such as lenses have revolutionized the ability collect medically relevant data easily. Even if the data has relatively low resolution or signal to noise ratio, newer algorithms have made it possible to identify disease with this data. Many low-cost diagnostic tools have been created in medical fields spanning from neurology to dermatology to obstetrics. These tools are particularly useful in low-resource areas where access to expensive diagnostic equipment may not be possible. The ultimate goal would be the creation of a “diagnostic toolkit” consisting of a smartphone and a set of sensors and attachments that can be used to screen for a wide set of diseases in a community healthcare setting. However, there are a few concerns that still need to be overcome in low-cost diagnostics: lack of incentives to bring these devices to market, algorithmic bias, “black box” nature of the algorithms, and data storage/transfer concerns.

Accuracy assessment of the geomorphon approach to detect ecological sites in the Dry Chaco region of Argentina

Ecological sites are a classification of local-land types based on differences in important environmental factors including soil properties, slope and landscape position within a geomorphologic and climatic zone. The concept is pivotal to conduct adaptative management in arid rangelands because it defines homogeneous areas over which specific soil-vegetation-disturbance dynamic occurs. Geomorphon is a computationally efficient method that uses Digital Elevation Models (DEMs) for the classification of landforms at multiple scales. The objective of this study was to evaluate the accuracy of the geomorphon approach to map ecological sites in a 11,259-ha study area within the Dry Chaco region of Argentina, utilizing local topographic positions along soil catena as a predictor of broader ecological sites classes. We used two DEM pixel resolutions (12.5 m and 30 m) and optimized the flatness threshold (t) and search radius (L) geomorphon parameters. As “ground truth” we used legacy data of 62 soil profiles descriptions. The geomorphon elements detected were reclassified into the three main ecological sites of the study area: highlands, midlands and lowlands. We used the overall disagreement (D) as the main metric for evaluating the accuracy of ecological site classifications. We found that: i) the lowest t value (0.05°) captured subtle topographical changes and thus more effectively reflected the soil-landscape relationship, and ii) larger L values paired with the lower pixel resolution (30 m) diminished the impact of minor landforms, improving the accuracy of ecological site detections. We determined that the geomorphon model with the pixel resolution of 30 m, t-value of 0.05°, and L-value of 12 produced the ecological site classification map with the highest accuracy, achieving a moderate-high accuracy (D of 33.9 %). Our study suggests that the geomorphon approach shows potential for consistent, reproducible and easily updatable ecological site mapping over larger areas.

Multimodal Data-Driven Segmentation of Bone Metastasis Lesions in SPECT Bone Scans Using Deep Learning

Background: Patients with malignant tumors often develop bone metastases. SPECT bone scintigraphy is an effective tool for surveying bone metastases due to its high sensitivity, low-cost equipment, and radiopharmaceutical. However, the low spatial resolution of SPECT scans significantly hinders manual analysis by nuclear medicine physicians. Deep learning, a promising technique for automated image analysis, can extract hierarchal patterns from images without human intervention. Objective: To enhance the performance of deep learning-based segmentation models, we integrate textual data from diagnostic reports with SPECT bone scans, aiming to develop an automated analysis method that outperforms purely unimodal data-driven segmentation models. Methods: We propose a dual-path segmentation framework to extract features from bone scan images and diagnostic reports separately. In the first path, an encoder-decoder network is employed to learn hierarchical representations of features from SPECT bone scan images. In the second path, the Chinese version of the MacBERT model is utilized to develop a text encoder for extracting features from diagnostic reports. The extracted textual features are then fused with image features during the decoding stage in the first path, enhancing the overall segmentation performance. Results: Experimental evaluation conducted on real-world clinical data demonstrated the superior performance of the proposed segmentation model. Our model achieved a 0.0209 increase in the DSC (Dice Similarity Coefficient) score compared to the well-known U-Net model. Conclusions: The proposed multimodal data-driven method effectively identifies and isolates metastasis lesions in SPECT bone scans, outperforming existing classical deep learning models. This study demonstrates the value of incorporating textual data in the deep learning-based segmentation of lowresolution SPECT bone scans.

A Low-Cost Sensor Network for Monitoring Peatland

Peatlands across the world are vital carbon stores. However, human activities have caused the degradation of many sites, increasing their greenhouse gas emissions and vulnerability to wildfires. Comprehensive monitoring of peatlands is essential for their protection, tracking degradation and restoration, but current techniques are limited by cost, poor reliability and low spatial or temporal resolution. This paper covers the research, development, deployment and performance of a resilient and modular multi-purpose wireless sensor network as an alternative means of monitoring peatlands. The sensor network consists of four sensor nodes and a gateway and measures temperature, humidity, soil moisture, carbon dioxide and methane. The sensor nodes transmit measured data over LoRaWAN to The Things Network every 30 min. To increase the maximum possible deployment duration, a novel datastring encoder was implemented which reduced the transmitted datastring length by 23%. This system was deployed in a New Forest (Hampshire, UK) peatland site for two months and collected more than 7500 measurements. This deployment demonstrated that low-cost sensor networks have the potential to improve the temporal and spatial resolution of peatland emission monitoring beyond what is achievable with traditional monitoring techniques.

A Multi-Stage Progressive Pansharpening Network Based on Detail Injection with Redundancy Reduction

In the field of remote sensing image processing, pansharpening technology stands as a critical advancement. This technology aims to enhance multispectral images that possess low resolution by integrating them with high-spatial-resolution panchromatic images, ultimately producing multispectral images with high resolution that are abundant in both spatial and spectral details. Thus, there remains potential for improving the quality of both the spectral and spatial domains of the fused images based on deep-learning-based pansharpening methods. This work proposes a new method for the task of pansharpening: the Multi-Stage Progressive Pansharpening Network with Detail Injection with Redundancy Reduction Mechanism (MSPPN-DIRRM). This network is divided into three levels, each of which is optimized for the extraction of spectral and spatial data at different scales. Particular spectral feature and spatial detail extraction modules are used at each stage. Moreover, a new image reconstruction module named the DRRM is introduced in this work; it eliminates both spatial and channel redundancy and improves the fusion quality. The effectiveness of the proposed model is further supported by experimental results using both simulated data and real data from the QuickBird, GaoFen1, and WorldView2 satellites; these results show that the proposed model outperforms deep-learning-based methods in both visual and quantitative assessments. Among various evaluation metrics, performance improves by 0.92–18.7% compared to the latest methods.

Pion gravitational form factors in the QCD instanton vacuum. I

The pion form factors of the QCD energy-momentum tensor (EMT) are studied in the instanton liquid model (ILM) of the QCD vacuum. In this approach, the breaking of conformal symmetry is encoded in the form of stronger-than-Poisson fluctuations in the number of instantons. For the trace of the EMT, it is shown that the gluonic trace anomaly term contributes half the pion mass, with the other half coming from the quark-mass-dependent σ term. The Q2 dependence of the form factors is governed by glueball and scalar meson exchanges. For the EMT, the spin-0 (trace) and spin-2 (traceless rank-2 tensor) form factors are computed at next-to-leading order in the instanton density using effective quark operators. Relations between the gluon and quark contributions to the EMT form factors are derived. The form factors are also expressed in terms of the pion light-front wave functions in the ILM. The results at the low resolution scale of the inverse instanton size are evolved to higher scales using the renormalization group equation. The ILM results compare well with those of recent lattice QCD calculations. Published by the American Physical Society 2024

Improved Methods for Retrieval of Chlorophyll Fluorescence from Satellite Observation in the Far-Red Band Using Singular Value Decomposition Algorithm

Solar-induced chlorophyll fluorescence (SIF) is highly correlated with photosynthesis and can be used for estimating gross primary productivity (GPP) and monitoring vegetation stress. The far-red band of the solar Fraunhofer lines (FLs) is close to the strongest SIF emission peak and is unaffected by chlorophyll absorption, making it suitable for SIF intensity retrieval. In this study, we propose a retrieval window for far-red SIF, significantly enhancing the sensitivity of data-driven methods to SIF signals near 757 nm. This window introduces a weak O2 absorption band based on the FLs window, allowing for better separation of SIF signals from satellite spectra by altering the shape of specific singular vectors. Additionally, a frequency shift correction algorithm based on standard non-shifted reference spectra is proposed to discuss and eliminate the influence of the Doppler effect. SIF intensity retrieval was achieved using data from the GOSAT satellite, and the retrieved SIF was validated using GPP, enhanced vegetation index (EVI) from the MODIS platform, and published GOSAT SIF products. The validation results indicate that the SIF products provided in this study exhibit higher fitting goodness with GPP and EVI at high spatiotemporal resolutions, with improvements ranging from 55% to 129%. At low spatiotemporal resolutions, the SIF product provided in this study shows higher consistency with EVI and GPP spatially.

Long-term Tracking for High-frequency surface wave radar based on Heading Constraint Filtering combined ELM

Abstract Aiming at the problem of low measurement resolution and poor tracking accuracy in High-frequency surface wave radar (HFSWR) tracking field, a tracking method that combines the heading constraint filter and Extreme Learning Machine (ELM) is proposed. Compared with the existing research, the innovation of this study lies in proposing a two-stage tracking method based on the short-term stable state of the target, and similarity of long-term trajectory. Firstly, the heading constraint information is introduced into the estimator and improve the estimation accuracy. In multi-target tracking scenarios, many tracklets can be obtained. Then, the ELM learns robust features of tracklets to achieve the trajectory segment classification from the same target. This method can be widely applied to long-term tracking of cargo or passenger ships with fixed destinations, significantly improving tracking performance by utilizing implicit domain knowledge without additional information. The actual data of this paper comes from HFSWR on Bohai Bay. Both the simulation and actual measurement experiments show that the proposed cascaded tracking method achieves long-term continuous tracking, and generates more complete trajectories. Moreover, the proposed Extended Kalman filter based on pseudo-measurement and intercept parameters (IPM-EKF) exhibits better tracking stability and accuracy in limited scan steps. This study provides new ideas and methods for improving the tracking performance of special targets in the HFSWR field by utilizing motion features.

A First Extension of the Robust Satellite Technique RST-FLOOD to Sentinel-2 Data for the Mapping of Flooded Areas: The Case of the Emilia Romagna (Italy) 2023 Event

Extreme meteorological events hit our planet with increasing frequency, resulting in an ever-increasing number of natural disasters. Flash floods generated by intense and violent rains are among the most dangerous natural disasters that compromise crops and cause serious damage to infrastructure and human lives. In the case of such a kind of disastrous events, timely and accurate information about the location and extent of the affected areas can be crucial to better plan and implement recovery and containment interventions. Satellite systems may efficiently provide such information at different spatial/temporal resolutions. Several authors have developed satellite techniques to detect and map inundated areas using both Synthetic Aperture Radar (SAR) and a new generation of high-resolution optical data but with some accuracy limits, mostly due to the use of fixed thresholds to discriminate between the inundated and unaffected areas. In this paper, the RST-FLOOD fully automatic technique, which does not suffer from the aforementioned limitation, has been exported for the first time to the mid–high-spatial resolution (20 m) optical data provided by the Copernicus Sentinel-2 Multi-Spectral Instrument (MSI). The technique was originally designed for and successfully applied to Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) satellite data at a mid–low spatial resolution (from 1000 to 375 m). The processing chain was implemented in a completely automatic mode within the Google Earth Engine (GEE) platform to study the recent strong flood event that occurred in May 2023 in Emilia Romagna (Italy). The outgoing results were compared with those obtained through the implementation of an existing independent optical-based technique and the products provided by the official Copernicus Emergency Management Service (CEMS), which is responsible for releasing information during crisis events. The comparisons carried out show that RST-FLOOD is a simple implementation technique able to retrieve more sensitive and effective information than the other optical-based methodology analyzed here and with an accuracy better than the one offered by the CEMS products with a significantly reduced delivery time.

A deep learning approach for white blood cells image generation and classification using SRGAN and VGG19

The classification of White Blood Cells (WBCs) is crucial for diagnosing diseases, monitoring treatment effectiveness, and understanding how the immune system functions. In this paper, we propose a deep learning approach to classify WBCs using Super Resolution Generative Adversarial Network (SRGAN) and Visual Geometry Group 19 (VGG19). Firstly, microscopic images of WBCs are generated using the SRGAN to obtain more precise and high-resolution images, which are then classified with a pretrained VGG19 classifier. Low-resolution (LR) images are inputted into the generator of SRGAN, and its discriminator compares the High-resolution (HR) image with LR, generating super-resolution images to minimize misclassification risks. A large dataset of 12,447 images containing four classes of WBCs (Eosinophil, Lymphocyte, Monocyte, and Neutrophil) is utilized to train and validate our proposed model. Following extensive experimental analysis, our proposed model achieves a test accuracy of 94.87 %, surpassing traditional Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Hybrid CNN-RNN models, and other conventional approaches. The generated images of SRGAN overcome challenges associated with misclassification due to the poor resolution of microscopic images, while the use of a pretrained model as a classifier reduces classification complexity. The source code of the entire work is available at https://github.com/Jannatul-Ferdousi/SRGAN_VGG19_WBC.git.

Application of a Real-Time Field-Programmable Gate Array-Based Image-Processing System for Crop Monitoring in Precision Agriculture

Precision agriculture (PA) technologies combined with remote sensors, GPS, and GIS are transforming the agricultural industry while promoting sustainable farming practices with the ability to optimize resource utilization and minimize environmental impact. However, their implementation faces challenges such as high computational costs, complexity, low image resolution, and limited GPS accuracy. These issues hinder timely delivery of prescription maps and impede farmers’ ability to make effective, on-the-spot decisions regarding farm management, especially in stress-sensitive crops. Therefore, this study proposes field programmable gate array (FPGA)-based hardware solutions and real-time kinematic GPS (RTK-GPS) to develop a real-time crop-monitoring system that can address the limitations of current PA technologies. Our proposed system uses high-accuracy RTK and real-time FPGA-based image-processing (RFIP) devices for data collection, geotagging real-time field data via Python and a camera. The acquired images are processed to extract metadata then visualized as a heat map on Google Maps, indicating green area intensity based on romaine lettuce leafage. The RFIP system showed a strong correlation (R2 = 0.9566) with a reference system and performed well in field tests, providing a Lin’s concordance correlation coefficient (CCC) of 0.8292. This study demonstrates the potential of the developed system to address current PA limitations by providing real-time, accurate data for immediate decision making. In the future, this proposed system will be integrated with autonomous farm equipment to further enhance sustainable farming practices, including real-time crop health monitoring, yield assessment, and crop disease detection.

Low Resolution MRI Images Privacy Feature Mapping and Classification Using LSTM-CNN Models in IoMT Healthcare Applications

Low Resolution MRI Images Privacy Feature Mapping and Classification Using LSTM-CNN Models in IoMT Healthcare Applications

PSVI-6 Genomic determinants of the rumen microbiome and metabolome in Angus steers

Abstract The rumen microbiome provides approximately 70% of energy required for the ruminant host through fermentation byproducts. Due to microbial impacts on host health and productivity, several studies have associated microbiome and metabolome variation with important sustainability phenotypes, such as feed efficiency and methane emissions. Recent studies have indicated the microbial communities present in rumen fluid are under low-to-moderate host genetic control. However, microorganisms present on the rumen wall have been underrepresented in research, negating their unique role in host-microbiome interactions. Current studies investigating heritability of the rumen planktonic microorganisms are plagued by small sample sizes, high management and breed heterogeneity, and low resolution for classifying microbial communities. To determine the heritability of the rumen fluid and epithelium-associated microbiome and metabolome, Angus steers (n = 350) were enrolled in a 70-d feed efficiency trial, where real-time dry matter intake was measured. Body weights were collected throughout the trial to calculate average daily gain and residual feed intake. Mid-trial, a novel surgery method was used to swab the rumen wall to capture epithelium-associated microorganisms. On d 70, 50 mL of rumen fluid was obtained from steers using orogastric tubing for collection of planktonic microbial communities. Metagenomic DNA extracted from both rumen fluid and epimural samples was used for reduced representation sequencing to predict microbial features such as diversity, composition, and putative function. Genomic DNA isolated from whole blood was used to perform low-pass whole genome sequencing for use in downstream heritability estimates and variant association analyses. Many core microbial abundances are under low-to-moderate host genetic control (h2 > 0.15; P < 0.05); however, many microorganisms had low heritability estimates (h2 < 0.10). Preliminary genome-wide associations studies are vastly underpowered to detect host genetic variants associated with microbial features, but data generated using the total number of animals will better refine these polymorphisms. Several key heritable metabolites were identified in amino acid metabolism pathways in rumen fluid (P < 0.05). The rumen microbiome appears to act as a complex polygenic trait and incorporating microbial features into prediction models may improve feed efficiency estimations. Understanding host genetic control of the rumen microbiome and metabolome may empower downstream applications to enhance genomic and phenotypic predictions for feed efficiency and other sustainability traits in beef cattle.

Analysis of Complexome Profiles with the Gaussian Interaction Profiler (GIP) Reveals Novel Protein Complexes in Plasmodium falciparum.

Complexome profiling is an experimental approach to identify interactions by integrating native separation of protein complexes and quantitative mass spectrometry. In a typical complexome profile, thousands of proteins are detected across typically ≤100 fractions. This relatively low resolution leads to similar abundance profiles between proteins that are not necessarily interaction partners. To address this challenge, we introduce the Gaussian Interaction Profiler (GIP), a Gaussian mixture modeling-based clustering workflow that assigns protein clusters by modeling the migration profile of each cluster. Uniquely, the GIP offers a way to prioritize actual interactors over spuriously comigrating proteins. Using previously analyzed human fibroblast complexome profiles, we show good performance of the GIP compared to other state-of-the-art tools. We further demonstrate GIP utility by applying it to complexome profiles from the transmissible lifecycle stage of malaria parasites. We unveil promising novel associations for future experimental verification, including an interaction between the vaccine target Pfs47 and the hypothetical protein PF3D7_0417000. Taken together, the GIP provides methodological advances that facilitate more accurate and automated detection of protein complexes, setting the stage for more varied and nuanced analyses in the field of complexome profiling. The complexome profiling data have been deposited to the ProteomeXchange Consortium with the dataset identifier PXD050751.

SRARDA: A lightweight adaptive residual dense attention generative adversarial network for image super-resolution

Image super-resolution (SR) is the task of inferring a high resolution (HR) image from one/multiple single low resolution (LR) input(s). Traditional networks are evaluated by pixel-level metrics such as Peak-Signal-to-Noise Ratio (PSNR) etc., which do not always align with human perception of image quality. They often produce excessively smooth images that lack high-frequency texture and appear unnatural. Therefore, in this paper, we propose a lightweight adaptive residual dense attention generative adversarial network (SRARDA) for image SR. Firstly, our generator adopts the residual in residual (RIR) structure but redesigns the basic module. By using dynamic residual connection (ARC) to dynamically adjust the importance of residual and main paths, we design a novel adaptive residual dense attention block (ARDAB) that enhances the feature extraction capability of the generator. In addition, we build a high-frequency filtering unit (HFU) to extract more high-frequency features from the LR space. Finally, to fully utilize the discriminator, we use WGAN to compute the difference between the HR image and the reconstructed image. Experiments demonstrate that SRARDA effectively addresses the issue of excessive smoothing in reconstructed images, while also enhancing visual quality.

Lightweight image super-resolution with sliding Proxy Attention Network

Recently, image super-resolution (SR) models using window-based Transformers have demonstrated superior performance compared to SR models based on convolutional neural networks. Nevertheless, Transformer-based SR models often entail high computational demands. This is due to the adoption of shifted window self-attention following the window self-attention layer to model long-range relationships, resulting in additional computational overhead. Moreover, extracting local image features only with the self-attention mechanism is insufficient to reconstruct rich high-frequency image content. To overcome these challenges, we propose the Sliding Proxy Attention Network (SPAN), capable of recovering high-quality High-Resolution (HR) images from Low-Resolution (LR) inputs with substantially fewer model parameters and computational operations. The primary innovation of SPAN lies in the Sliding Proxy Transformer Block (SPTB), integrating the local detail sensitivity of convolution with the long-range dependency modeling of self-attention mechanism. Key components within SPTB include the Enhanced Local Feature Extraction Block (ELFEB) and the Sliding Proxy Attention Block (SPAB). ELFEB is designed to enhance the local receptive field with lightweight parameters for high-frequency details compensation. SPAB optimizes computational efficiency by implementing intra-window and cross-window attention in a single operation through leveraging window overlap. Experimental results demonstrate that SPAN can produce high-quality SR images while effectively managing computational complexity. The code is publicly available at: https://github.com/zononhzy/SPAN.

Graphene-functionalized photonic crystal fibers for detecting the refractive index of a wide range of analyzes

This study looks into a new photonic crystal fiber (PCF) sensor covered in graphene that can detect changes in the analyte's refractive index (RI) in the COMSOL environment. The sensor operates by detecting a peak in light loss that varies in wavelength based on the RI of the analyte. Higher RI analytes shift the peak towards longer wavelengths. It is explored how many factors affect the sensor's performance, such as graphene's Fermi energy, the site inside the fiber, and the thickness of the gold layer. The location of graphene and its Fermi energy have a substantial influence on how the sensor responds. Interestingly, the resonant wavelength has a linear relationship with the RI of the analyte, which allows for precise RI calculation. Increasing gold thickness improves light-material interaction, but subsequent thickening has a detrimental impact, lowering sensor sensitivity. The appropriate thickness varies according to the material under consideration. Finally, the analysis determines the optimal arrangement of graphene for maximum sensitivity by completely covering the analyte holes in the PCF. By finding the right balance between these factors, the suggested sensor can have both high spectral sensitivity and low sensor resolution.

Double Tips for In-Plane Polarized Near-Field Microscopy and Spectroscopy.

Near-field optical microscopy and spectroscopy provide high-resolution imaging below the diffraction limit, crucial in physics, chemistry, and biology for studying molecules, nanoparticles, and viruses. These techniques use a sharp metallic tip of an atomic force microscope (AFM) to enhance incoming and scattered light by excited near-fields at the tip apex, leading to high sensitivity and a spatial resolution of a few nanometers. However, this restricts the near-field orientation to out-of-plane polarization, limiting optical polarization choices. We introduce double tips that offer in-plane polarization for enhanced imaging and spectroscopy. These double tips provide superior enhancement over single tips, although with a slightly lower spatial resolution (∼30 nm). They enable advanced studies of nanotubes, graphene defects, and transition metal dichalcogenides, benefiting from polarization control. The double tips allow varied polarization in tip-enhanced Raman scattering and selective excitation of transverse-electric and -magnetic polaritons, expanding the range of nanoscale samples that can be studied.

Steric Sea Level Rise and Relationships with Model Drift and Water Mass Representation in GFDL CM4 and ESM4

Abstract Density-driven steric seawater changes are a leading-order contributor to global mean sea level rise. However, inter-model differences in the magnitude and spatial patterns of steric sea level rise exist at regional scales and often emerge during the spin-up and pre-industrial control integrations of climate models. Steric sea level results from an eddy-permitting climate model, GFDL-CM4, are compared with a lower resolution counterpart, GFDL-ESM4. The results from both models are examined through basin-scale heat budgets and watermass analysis, and we compare the patterns of ocean heat uptake, redistribution, and sea level differ in ocean-only (i.e. OMIP) and coupled climate configurations. After correcting for model drift, both GFDL-CM4 and GFDL-ESM4 simulate nearly equivalent ocean heat content change and global sea level rise during the historical period. However, the GFDL-CM4 model exhibits as much as a 40% increase in surface ocean heat uptake in the Southern Ocean and subsequent increases in horizontal export to other ocean basins after bias correction. The results suggest regional differences in the processes governing Southern Ocean heat export, such as the formation of AAIW, SPMW, and gyre transport between the two models, and that sea level changes in these models cannot be fully bias-corrected. Since the process-level differences between the two models are evident in the preindustrial control simulations of both models, these results suggest that the control simulations are important for identifying and correcting sea-level related model biases.