Missing Content Research Articles

Image Restoration Technology of Tang Dynasty Tomb Murals Using Adversarial Edge Learning

The city of Xi’an, China houses a vast collection of valuable Tang dynasty (618 AD-907 AD) tomb murals that have experienced various degrees of damage over time due to weathering. This research proposes an adversarial edge learning-based mural restoration technique that provides a fast and accurate automatic repair of the murals. The method uses generative adversarial networks to learn how to repair the edge contour of a damaged mural and restore the missing content. The contour restoration network and mural restoration networks are trained and updated simultaneously to enhance the feature extraction and restoration coordination performance of the network. The experiments were conducted on a self-built Tang dynasty tomb mural painting restoration dataset and compared to several existing algorithms. The proposed algorithm showed a 1.3 increase in peak signal-to-noise ratio (PSNR) and a 1.43% increase in structural similarity (SSIM) value, providing a precise restoration of the structure and contour of the mural content. The algorithm successfully repaired damaged murals with fractures and small missing parts. The suggested method can be useful for both the technical repair and conservation of significant national visual cultural property and the digital restoration of historic murals.

Exploring Theoretical Issues on Teaching Natural Science Subjects in Grade 6 at Secondary Schools in the Context of Vietnam

Teaching Natural Science subjects in grade 6 at Secondary schools can be understood as the process of impact of teachers teaching Natural Science subjects on grade 6 pupils, through educational activities, to impart knowledge of Natural Science, to achieve academic objectives according to the regulations of the general education program. Studies have shown that teaching Natural Science subjects in grade 6 at Secondary schools is designed to develop the qualities and abilities of Natural Science for grade 6 pupils. This educational activity has achieved some positive results in the past but still reveals some limitations. Exploring theoretical issues in teaching Natural Science subjects in grade 6 at Secondary schools will help identify necessary content and orientation for practical activities. Based on the assessment of legal documents of the state; research selectively inherits in-depth research of several researchers; analyze, compare, synthesize, and evaluate; this study has discovered theoretical issues on teaching activities of Natural Science subjects in grade 6 at Secondary schools as position, role, objectives, content, methods, forms as well as conditions to ensure teaching activities of Natural Science subjects in grade 6 at Secondary schools. Discovering these contents has clarified and supplemented the missing content on theoretical issues; at the same time, it is the basis for applying to educational practice at Secondary schools.

Monocular BEV Perception of Road Scenes via Front-to-Top View Projection.

HD map reconstruction is crucial for autonomous driving. LiDAR-based methods are limited due to expensive sensors and time-consuming computation. Camera-based methods usually need to perform road segmentation and view transformation separately, which often causes distortion and missing content. To push the limits of the technology, we present a novel framework that reconstructs a local map formed by road layout and vehicle occupancy in the bird's-eye view given a front-view monocular image only. We propose a front-to-top view projection (FTVP) module, which takes the constraint of cycle consistency between views into account and makes full use of their correlation to strengthen the view transformation and scene understanding. In addition, we apply multi-scale FTVP modules to propagate the rich spatial information of low-level features to mitigate spatial deviation of the predicted object location. Experiments on public benchmarks show that our method achieves various tasks on road layout estimation, vehicle occupancy estimation, and multi-class semantic estimation, at a performance level comparable to the state-of-the-arts, while maintaining superior efficiency.

A Qualitative Evaluation of ChatGPT4 and PaLM2's Response to Patient's Questions Regarding Age-Related Macular Degeneration.

Patient compliance in chronic illnesses is essential for disease management. This also applies to age-related macular degeneration (AMD), a chronic acquired retinal degeneration that needs constant monitoring and patient cooperation. Therefore, patients with AMD can benefit by being properly informed about their disease, regardless of the condition's stage. Information is essential in keeping them compliant with lifestyle changes, regular monitoring, and treatment. Large language models have shown potential in numerous fields, including medicine, with remarkable use cases. In this paper, we wanted to assess the capacity of two large language models (LLMs), ChatGPT4 and PaLM2, to offer advice to questions frequently asked by patients with AMD. After searching on AMD-patient-dedicated websites for frequently asked questions, we curated and selected a number of 143 questions. The questions were then transformed into scenarios that were answered by ChatGPT4, PaLM2, and three ophthalmologists. Afterwards, the answers provided by the two LLMs to a set of 133 questions were evaluated by two ophthalmologists, who graded each answer on a five-point Likert scale. The models were evaluated based on six qualitative criteria: (C1) reflects clinical and scientific consensus, (C2) likelihood of possible harm, (C3) evidence of correct reasoning, (C4) evidence of correct comprehension, (C5) evidence of correct retrieval, and (C6) missing content. Out of 133 questions, ChatGPT4 received a score of five from both reviewers to 118 questions (88.72%) for C1, to 130 (97.74%) for C2, to 131 (98.50%) for C3, to 133 (100%) for C4, to 132 (99.25%) for C5, and to 122 (91.73%) for C6, while PaLM2 to 81 questions (60.90%) for C1, to 114 (85.71%) for C2, to 115 (86.47%) for C3, to 124 (93.23%) for C4, to 113 (84.97%) for C5, and to 93 (69.92%) for C6. Despite the overall high performance, there were answers that are incomplete or inaccurate, and the paper explores the type of errors produced by these LLMs. Our study reveals that ChatGPT4 and PaLM2 are valuable instruments for patient information and education; however, since there are still some limitations to these models, for proper information, they should be used in addition to the advice provided by the physicians.

WaveFormer: Wavelet Transformer for Noise-Robust Video Inpainting

Video inpainting aims to fill in the missing regions of the video frames with plausible content. Benefiting from the outstanding long-range modeling capacity, the transformer-based models have achieved unprecedented performance regarding inpainting quality. Essentially, coherent contents from all the frames along both spatial and temporal dimensions are concerned by a patch-wise attention module, and then the missing contents are generated based on the attention-weighted summation. In this way, attention retrieval accuracy has become the main bottleneck to improve the video inpainting performance, where the factors affecting attention calculation should be explored to maximize the advantages of transformer. Towards this end, in this paper, we theoretically certificate that noise is the culprit that entangles the process of attention calculation. Meanwhile, we propose a novel wavelet transformer network with noise robustness for video inpainting, named WaveFormer. Unlike existing transformer-based methods that utilize the whole embeddings to calculate the attention, our WaveFormer first separates the noise existing in the embedding into high-frequency components by introducing the Discrete Wavelet Transform (DWT), and then adopts clean low-frequency components to calculate the attention. In this way, the impact of noise on attention computation can be greatly mitigated and the missing content regarding different frequencies can be generated by sharing the calculated attention. Extensive experiments validate the superior performance of our method over state-of-the-art baselines both qualitatively and quantitatively.

Fragmented Hours: The Biography of a Printed Devotional Book by Thielman Kerver

As the most richly illustrated and widely-owned texts of the late medieval and early modern eras, books of hours are essential to the study of art, religion, and the history of the book. Fragmented and altered books, while perhaps less coveted, are of particular value for what they may reveal of book owners and the changing meanings and uses of devotional texts and images over more than five centuries. This paper explores the compelling biography of a book of hours in the University of Florida Library that has undergone such extensive alteration prior to its acquisition in 1989 that cataloguers could not identify the printer and edition, leaving the book’s many dislocations, redactions, and annotations unexplored. Engaging with scholarship on the social history of books of hours, I identify the fragmented book as possibly the sole surviving copy of an edition produced by Thielman Kerver in Paris around 1510 and reconstruct its missing contents through comparison with relevant editions. Next, I examine the book’s complex web of redactions, erasures, and annotations in the context of sixteenth-century religious reform before turning to the book’s dislocations and the spoliation of images in the context of nineteenth and twentieth-century collecting trends.

朴世采의 『東儒師友錄』 사본과 구성에 대한 검토

In this article, we focused on the fact that Park Se-chae's 《Dongyoo saurok》 exists in various forms of manuscripts and compared each manuscript. 《Dongyoo saurok》 was a hagiography of Eastern Confucians compiled by Park Se-chae throughout his life with the purpose of preserving records of the origins of Confucianism in the East. Park Se-chae's munin Lee Se-hwan continued his teacher's work, but was unable to publish it. Afterwards, 《Dongyoo saurok》 was distributed in manuscript form. The extant manuscripts of 《Dongyoo saurok》 can be roughly divided into two types. First, there were early manuscripts of 20 fascicles in 10 volumes compiled by Park Se-chae, and second, there were later manuscripts edited by Lee Se-hwan. 《Dongyoo saurok》 encompassed about 700 Eastern Confucian scholars, from Seol chong and Choi Chi-won of Silla to munin of Yi I and Seong hon of Joseon, and it clearly revealed the relationship between teacher and munin. In particular, Park Se-chae attempted to highlight the status of the two schools as the pinnacle of Eastern Confucianism by including Yi I and Seong hon and their munin as the most recent school of 《Dongyoo saurok》. In 《Dongyoo saurok》, a total of about 200 books were used, along with a variety of sentences for which no bibliographic reference was recorded. Among 《Dongyoo saurok》, the munin of Yi I and Seong hon had missing content due to limitations in individual manuscripts. Through comparison between manuscripts, the munin of the two schools were supplemented, and the munin of Yi I and Seong Hon were reconstituted with 85 and 112 people, respectively.

Local-to-Global Panorama Inpainting for Locale-Aware Indoor Lighting Prediction.

Predicting panoramic indoor lighting from a single perspective image is a fundamental but highly ill-posed problem in computer vision and graphics. To achieve locale-aware and robust prediction, this problem can be decomposed into three sub-tasks: depth-based image warping, panorama inpainting and high-dynamic-range (HDR) reconstruction, among which the success of panorama inpainting plays a key role. Recent methods mostly rely on convolutional neural networks (CNNs) to fill the missing contents in the warped panorama. However, they usually achieve suboptimal performance since the missing contents occupy a very large portion in the panoramic space while CNNs are plagued by limited receptive fields. The spatially-varying distortion in the spherical signals further increases the difficulty for conventional CNNs. To address these issues, we propose a local-to-global strategy for large-scale panorama inpainting. In our method, a depth-guided local inpainting is first applied on the warped panorama to fill small but dense holes. Then, a transformer-based network, dubbed PanoTransformer, is designed to hallucinate reasonable global structures in the large holes. To avoid distortion, we further employ cubemap projection in our design of PanoTransformer. The high-quality panorama recovered at any locale helps us to capture spatially-varying indoor illumination with physically-plausible global structures and fine details.

Image Inpainting Based on Multi-Level Feature Aggregation Network for Future Internet

(1) Background: In the future Internet era, clarity and structural rationality are important factors in image inpainting. Currently, image inpainting techniques based on generative adversarial networks have made great progress; however, in practical applications, there are still problems of unreasonable or blurred inpainting results for high-resolution images and images with complex structures. (2) Methods: In this work, we designed a lightweight multi-level feature aggregation network that extracts features from convolutions with different dilation rates, enabling the network to obtain more feature information and recover more reasonable missing image content. Fast Fourier convolution was designed and used in the generative network, enabling the generator to consider the global context at a shallow level, making it easier to perform high-resolution image inpainting tasks. (3) Results: The experiment shows that the method designed in this paper performs well in geometrically complex and high-resolution image inpainting tasks, providing a more reasonable and clearer inpainting image. Compared with the most advanced image inpainting methods, our method outperforms them in both subjective and objective evaluations. (4) Conclusions: The experimental results indicate that the method proposed in this paper has better clarity and more reasonable structural features.

The Invisibility of Scotomas I: The Carving Hypothesis.

Veridical depictions of scene appearance with scotomas allow better understanding of the impact of field loss and may improve the development and implementation of rehabilitation. Explanation and depiction of the invisibility of scotoma may lead to patients' understanding and thus better compliance with related treatments. Simulations of perception with scotomas guide training, patient education, and rehabilitation research. Most simulations incorrectly depict scotomas as black patches, although the scotomas and the missing contents are usually invisible to patients. We present a novel approach to capture the reported appearance of scenes with scotomas. We applied a content-aware image resizing algorithm to carve out the content elided under the scotomas. With video sequences, we show how and why eye movements fail to increase the visibility of the carved scotomas. Numerous effects, reported by patients, emerge naturally from the scotoma carving. Carving-eliminated scotomas over natural images are barely visible, despite causing substantial distortions. Low resolution and contrast sensitivity at farther eccentricities and saccadic blur reduce the visibility of the distortions. In a walking scenario, static objects moving smoothly to the periphery disappear into and then reemerge out of peripheral scotomas, invisibly. Scotoma carving provides a viable hypothetical simulation of vision with scotomas due to loss of neurons at the retinal ganglion cell level and higher. As a hypothesis, it generates predictions that lend themselves to future clinical testing. The different effects of scotomas due to loss of photoreceptors are left for follow-up work.

Semi-supervised progressive dehazing network using unlabeled contrastive guidance

Image dehazing aims to restore the missing high-quality content from its original hazy observation. Most of the existing learning-based methods achieve promising achievements by designing various networks. However, these approaches cannot generalize well on real-world scenes, since they fail to exploit natural haze priors. Towards this end, we propose a novel Semi-supervised Progressive Dehazing Network (Semi-PDNet), which leverages both synthetic and real-world images in training process. The overall network follows a progressive architecture, and can be divided into three core stages: image encode stage (IES), feature enhance stage (FES) and hierarchical reconstruction stage (HRS). Specifically, IES is responsible for encoding shallow features from the corrupted hazy image. Then, our FES tries to distill finer local and global features via the well-designed dual stream attentive block (DSAB). The HRS is to estimate semantic and contextual information based on a hierarchical structure, and accurately reconstructs the final clear image. This stage-by-stage paradigm can make full use of informative features from shallow to deep, thus facilitating network for better haze removal. Furthermore, we utilize an unlabeled contrastive guidance (UCG) to bridge the domain gap between synthetic and real-world images. Extensive experimental comparisons show that our Semi-PDNet can obtain comparable results with other state-of-the-art dehazing algorithms.

Ancient mural inpainting via structure information guided two-branch model

Ancient murals are important cultural heritages for our exploration of ancient civilizations and are of great research value. Due to long-time exposure to the environment, ancient murals often suffer from damage (deterioration) such as cracks, scratches, corrosion, paint loss, and even large-region falling off. It is an urgent work to protect and restore these damaged ancient murals. Mural inpainting techniques refer to virtually filling the deteriorated regions by reconstructing the structure and texture elements of the mural images. Most existing mural inpainting approaches fail to fill loss contents that contain complex structures and diverse patterns since they neglect the importance of structure guidance. In this paper, we propose a structure-guided two-branch model based on the generative adversarial network (GAN) for ancient mural inpainting. In the proposed model, the mural inpainting process can be divided into two stages: structure reconstruction and content restoration. These two stages are conducted by using a structure reconstruction network (SRN) and a content restoration network (CRN), respectively. In the structure reconstruction stage, SRN employs the Gated Convolution and the Fast Fourier Convolution (FFC) residual block to reconstruct the missing structures of the damaged murals. In the content restoration stage, CRN uses the structures (generated by SRN) to guide the missing content restoration of the murals. We design a two-branch parallel encoder to improve the texture and color restoration quality for the missing regions of the murals. Moreover, we propose a cascade attention module that can capture long-term relevance information in the deep features. It helps to alleviate the texture-blur and color-bias problem. We conduct experiments on both simulated and real damaged murals, and compare our inpainting results with other four competitive approaches. Experimental results show that our proposed model outperforms other approaches in terms of texture clarity, color consistency and structural continuity of the restored mural images. In addition, the mural inpainting results of our model can achieve comparatively high quantitative evaluation metrics.

Text Guided Image Using Machine Learning

bstract: Given a corrupted image, image inpainting aims to complete the image and outputs a plausible result. When we complete the missing region, we always borrow the information from a known area, which is aimless and causes unsatisfactory results. In our daily life, some other information is often used for corrupted images, such as text descriptions. Therefore, we introduce the use of text information to guide image inpainting. To fullfill this task, We introduce an inpainting model named TG Net (Text-Guided Inpainting Network). We provide a text-image gated feature fusion module to fuse text feature and image feature deeply. A mask attention module is proposed to enhance the consistency of known areas and the repaired area. Extensive quantitative and qualitative experiments on three public datasets with captions demonstrate the effectiveness of our method. The goal of our paper is to semantically edit parts of an image matching a given text that describes desired attributes (e.g., texture, colour, and background), while preserving other contents that are irrelevant to the text. To achieve this, we propose a novel generative adversarial network (Mani GAN), which contains two key components: text-image affine combination module (ACM) and detail correction module (DCM). The ACM selects image regions relevant to the given text and then correlates the regions with corresponding semantic words for effective manipulation. Meanwhile, it encodes original image features to help reconstruct text-irrelevant contents. The DCM rectifies mismatched attributes and completes missing contents of the synthetic image. Finally, we suggest a new metric for evaluating image manipulation results, in terms of both the generation of new attributes and the reconstruction of text-irrelevant contents. Extensive experiments on the CUB and COCO datasets demonstrate the superior performance of the proposed method. Code is available at this https URL.

The effect of free‐surface reflections on depth images for acquisition with buried sources and receivers: Investigation through the point spread function#

AbstractIn reverse time migration, if sources or receivers are buried at certain depths, the interference between the primary wave and the free‐surface reflection causes losing spectral contents in seismic data. If they are not properly compensated, errors will be carried to the target during the reverse time migration and cause a distorted image by so‐called ghost image. By invoking the point spread function, we investigate how distorted seismic data are mapped to the subsurface. Quantitative relations between the free‐surface parameters (i.e. source/receiver depth, free‐surface incident angle and near‐surface velocity) and wavenumber‐domain illumination can be created, which map missing frequency contents in seismic data to missing wavenumber contents in the point spread function. After transformed back to the space domain, we can investigate its effect on the image distortion. Numerical calculations expand the above approach to handle complex overburden structures and more realistic acquisition geometries. The proposed method provides us with a useful tool to investigate deghosting related problems, for example optimizing parameters of acquisition geometry, evaluating capabilities of existing acquisition systems and data processing technologies in suppressing the free‐surface reflection effect in the depth image, or developing new deghosting approaches.

Learning Enriched Features for Fast Image Restoration and Enhancement.

Given a degraded input image, image restoration aims to recover the missing high-quality image content. Numerous applications demand effective image restoration, e.g., computational photography, surveillance, autonomous vehicles, and remote sensing. Significant advances in image restoration have been made in recent years, dominated by convolutional neural networks (CNNs). The widely-used CNN-based methods typically operate either on full-resolution or on progressively low-resolution representations. In the former case, spatial details are preserved but the contextual information cannot be precisely encoded. In the latter case, generated outputs are semantically reliable but spatially less accurate. This paper presents a new architecture with a holistic goal of maintaining spatially-precise high-resolution representations through the entire network, and receiving complementary contextual information from the low-resolution representations. The core of our approach is a multi-scale residual block containing the following key elements: (a) parallel multi-resolution convolution streams for extracting multi-scale features, (b) information exchange across the multi-resolution streams, (c) non-local attention mechanism for capturing contextual information, and (d) attention based multi-scale feature aggregation. Our approach learns an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details. Extensive experiments on six real image benchmark datasets demonstrate that our method, named as MIRNet-v2, achieves state-of-the-art results for a variety of image processing tasks, including defocus deblurring, image denoising, super-resolution, and image enhancement. The source code and pre-trained models are available at https://github.com/swz30/MIRNetv2.

Political Improved Invasive Weed Optimization-Driven Hybrid Exemplar Technique for Video Inpainting Process

Video inpainting aspires to fill the Spatio-temporal holes in videos with probable and coherent content. This process recovers the missing content of corrupted video effectively, which is useful in many fields, including removal of watermarking and video restoration. The difficulties of creating video contents with exquisite detail while maintaining spatiotemporal coherence in the missing areas is the main difficulty in the video inpainting process. Modern studies ignore semantic structural coherence maintenance between frames in favor of using flow information to synthesize temporally smooth pixels. In this paper, Political Improved Invasive Weed Optimization (PIIWO)-based optimal exemplar is designed for the productive video inpainting process. Accordingly, the developed PIIWO algorithm is newly designed by combining Political Optimizer (PO) and Improved Invasive Weed Optimization (IIWO). Here, the inpainting results obtained from context-aware Ant Lion Gray Wolf Optimization (ALGWO)-based Markov Random Field (MRF) modeling, Whale Monarch Butterfly Optimization (Whale MBO)-based Deep Convolutional Neural Network (DCNN), K-Nearest Neighbors (KNN) with Bhattacharya distance, Bi-harmonic function modules and developed PIIWO-based exemplar model are fused using Bayes probabilistic fusion for producing the final result. Three metrics, peak signal-to-noise ratio (PSNR), second derivative like the measure of enhancement (SDME) and structural similarity (SSIM) of 40.19[Formula: see text]dB, 78.07[Formula: see text]dB and 0.9857, respectively, are used to assess the performance of the developed video inpainting technique.

Multichannel Cross-Scale Semantic Coherent Attention Network for Image Inpainting

This paper investigates a cross-scale space semantic feature coherent image inpainting approach since it is challenging for the existing image inpainting methods to fuse the semantic feature information effectively. Firstly, we learn the feature semantic relevance step-by-step from the high-level semantic feature map’s attention mechanism and then we apply what we have learned to the preceding low-level feature map. In order to preserve the visual and semantic coherence of image repair, the missing content can be filled by changing attention from deep to shallow in a multiscale manner. A broader receptive field is generated by partial convolution, and semantic feature relevance is achieved using a multiscale cross feature space feature attention mechanism based on semantic attention. This technique improves the extensibility and continuity of the restored images by reconstructing the semantic information of different feature spaces, not only taking into account the reuse of existing semantic space features but also including across feature spaces. The experimental results demonstrated an improvement in PSNR, SSIM, and L1 performance by 10.50%, 0.13%, and 47.09%, respectively, with clear benefits.

DRBR-HDR: Dual-Branch recursive band reconstruction network for HDR with large motions

Ghosting artifacts due to misaligned imaging and missing content of the moving regions are major challenges of synthesizing high dynamic range (HDR) images from multiple low-dynamic range (LDR) with different exposures in dynamic scenes. Therefore, it hopes the HDR reconstruction model can align the LDRs’ features and restore the missing content without artifacts. In the paper, a new dual-branch recursive band reconstruction network for high dynamic range (DRBR-HDR) is proposed to generate credible result in missing content regions, which not only uses global features as supplementary information to help local features from different receptive fields for efficient feature alignment but also designs a series of coarse-to-fine band representation to better repair missing areas in the process of recursion. In addition, we introduce an interactive attention mechanism for local branches to alleviate ghosting artifacts. The experimental results demonstrate that DRBR-HDR achieves state-of-the-art performance compared with that of the prevailing HDR reconstruction methods in various challenging scenes.Index Terms—inverse tone mapping, band reconstruction, global features, high dynamic range images.

Attack Resilience of Cache Replacement Policies: A Study Based on TTL Approximation

Caches are pervasively used in communication networks to speed up content access by reusing previous communications, where various replacement policies are used to manage the cached contents. The replacement policy of a cache plays a key role in its performance, and is thus extensively engineered to achieve a high hit ratio in benign environments. However, some studies showed that a policy with a higher hit ratio in benign environments may be more vulnerable to cache pollution attacks that intentionally send requests for unpopular contents. To understand the cache performance under such attacks, we analyze a suite of representative replacement policies under the framework of TTL approximation in how well they preserve the hit ratios for legitimate users, while incorporating the delay for the cache to obtain a missing content. We further develop a scheme to adapt the cache replacement policy based on the perceived level of attack. Our analysis and validation on real traces show that although no single policy is resilient to all the attack strategies, suitably adapting the replacement policy can notably improve the attack resilience of the cache. Motivated by these results, we implement selected policies as well as policy adaptation in an open-source SDN switch to manage flow rule replacement, which is shown to notably improve its resilience to pollution attacks.

Left Ventricular Diastolic Dysfunction and Progression of Chronic Kidney Disease: Analysis of KNOW-CKD Data.

BackgroundFew studies have examined the association between the early diastolic mitral inflow velocity/early diastolic mitral annulus velocity ratio (E/e’) and chronic kidney disease progression.Methods and ResultsWe reviewed data from 2238 patients with nondialysis chronic kidney disease from the KNOW‐CKD (Korean Cohort Study for Outcome in Patients With Chronic Kidney Disease); data from 163 patients were excluded because of missing content. A >50% decrease in estimated glomerular filtration rate from baseline, doubling of serum creatinine, or dialysis initiation and/or kidney transplantation were considered renal events. At baseline, median (interquartile range) ejection fraction and E/e’ were 64.0% (60.0%–68.0%) and 9.1 (7.4–11.9), respectively. Proportions of ejection fraction <50% and E/e’ ≥15 were 1.3% and 9.6%, respectively. More than one quarter of patients (27.2%) had an estimated glomerular filtration rate <30 mL/min per 1.73 m2. During the mean 59.1‐month follow‐up period, 724 patients (34.9%) experienced renal events. In multivariable Cox proportional hazard regression analysis, the hazard ratio with 95% CI per 1‐unit increase in E/e’ was 1.027 (1.005–1.050; P=0.016). Penalized spline curve analysis yielded a suggested threshold of E/e’ for renal events of 12; in our data set, the proportion of E/e’ ≥12 was 4.1%.ConclusionsIncreased E/e’ was associated with an increased hazard of renal events, suggesting that diastolic heart dysfunction is a novel risk factor for chronic kidney disease progression.