作者: neutral-posture 時間: 2025-3-21 22:44
Accurate and Efficient Single Image Super-Resolution with Matrix Channel Attention Network tasks. However, these methods are usually computationally expensive, which constrains their application in mobile scenarios. In addition, most of the existing methods rarely take full advantage of the intermediate features which are helpful for restoration. To address these issues, we propose a mod作者: incisive 時間: 2025-3-22 01:59
Second-Order Camera-Aware Color Transformation for Cross-Domain Person Re-identificationD remains a challenging task. The performance of ReID model trained on the labeled dataset (source) is often inferior on the new unlabeled dataset (target), due to large variation in color, resolution, scenes of different datasets. Therefore, unsupervised person ReID has gained a lot of attention du作者: 燈絲 時間: 2025-3-22 06:28
CS-MCNet: A Video Compressive Sensing Reconstruction Network with Interpretable Motion Compensation of video compressive sensing. Firstly, explicit multi-hypothesis motion compensation is applied in our network to extract correlation information of adjacent frames (as shown in Fig.?.), which improves the recover performance. And then, a residual module further narrows down the gap between reconst作者: FRAUD 時間: 2025-3-22 10:49 作者: 啪心兒跳動 時間: 2025-3-22 13:59 作者: 啪心兒跳動 時間: 2025-3-22 19:24 作者: 石墨 時間: 2025-3-22 23:13 作者: athlete’s-foot 時間: 2025-3-23 02:01
Robust High Dynamic Range (HDR) Imaging with Complex Motion and Parallaxtructing ghosting-free HDR images of dynamic scenes from a set of multi-exposure images is a challenging task, especially with large object motion, disparity, and occlusions, leading to visible artifacts using existing methods. In this paper, we propose a Pyramidal Alignment and Masked merging netwo作者: 人工制品 時間: 2025-3-23 09:07
Low-Light Color Imaging via Dual Camera Acquisitiono devise a dual camera system using a high spatial resolution (HSR) monochrome camera and another low spatial resolution (LSR) color camera for synthesizing the high-quality color image under low-light illumination conditions. The key problem is how to efficiently learn and fuse cross-camera informa作者: Conduit 時間: 2025-3-23 12:40 作者: Leaven 時間: 2025-3-23 14:07 作者: 悶熱 時間: 2025-3-23 20:12 作者: 一條卷發(fā) 時間: 2025-3-24 01:03
An Efficient Group Feature Fusion Residual Network for Image Super-Resolutionly to improve their performance by simply increasing the depth of their network. Although this strategy can get promising results, it is inefficient in many real-world scenarios because of the high computational cost. In this paper, we propose an efficient group feature fusion residual network (GFFR作者: 逢迎春日 時間: 2025-3-24 03:36 作者: conduct 時間: 2025-3-24 09:51 作者: Spartan 時間: 2025-3-24 13:09 作者: peptic-ulcer 時間: 2025-3-24 18:51
Multi-scale Attentive Residual Dense Network for Single Image Rain Removalhe investigation on rain removal has thus been attracting, while the performances of existing deraining have limitations owing to over smoothing effect, poor generalization capability and rain intensity varies both in spatial locations and color channels. To address these issues, we proposed a Multi作者: 事情 時間: 2025-3-24 19:10
Conference proceedings 2021n November/ December 2020.*.The total of 254 contributions was carefully reviewed and selected from 768 submissions during two rounds of reviewing and improvement. The papers focus on the following topics:..Part I: 3D computer vision; segmentation and grouping..Part II: low-level vision, image proce作者: EVEN 時間: 2025-3-24 23:23
Conference proceedings 2021for computer vision, generative models for computer vision..Part V: face, pose, action, and gesture; video analysis and event recognition; biomedical image analysis..Part VI: applications of computer vision; vision for X; datasets and performance analysis..*The conference was held virtually..作者: 者變 時間: 2025-3-25 07:22 作者: induct 時間: 2025-3-25 11:02 作者: N斯巴達人 時間: 2025-3-25 12:21 作者: insincerity 時間: 2025-3-25 17:15 作者: 裹住 時間: 2025-3-25 21:02
https://doi.org/10.1007/978-3-662-65102-5 network. Experimental results on large-scale dataset demonstrate the effectiveness of the proposed model against the state-of-the-art (SOTA) SR methods. Notably, when parameters are less than 320k, A.F outperforms SOTA methods for all scales, which proves its ability to better utilize the auxiliary features. Codes are available at ..作者: 防御 時間: 2025-3-26 03:21
Image Inpainting with Onion Convolutions an efficient implementation. As qualitative and quantitative comparisons show, our method with onion convolutions outperforms state-of-the-art methods by producing more realistic, visually plausible and semantically coherent results.作者: Obsessed 時間: 2025-3-26 06:37
CS-MCNet: A Video Compressive Sensing Reconstruction Network with Interpretable Motion Compensationo . better than state-of-the-art methods. In addition, due to the feed-forward architecture, the reconstruction can be processed by our network in real time and up?to three orders of magnitude faster than traditional iterative methods.作者: 設施 時間: 2025-3-26 09:16
Restoring Spatially-Heterogeneous Distortions Using Mixture of Experts Networkresentations. Our model is effective for restoring real-world distortions and we experimentally verify that our method outperforms other models designed to manage both single distortion and multiple distortions.作者: 脆弱么 時間: 2025-3-26 15:03
Overwater Image Dehazing via Cycle-Consistent Generative Adversarial Networklf-supervision and a perceptual loss for content preservation. In addition to qualitative evaluation, we design an image quality assessment network to rank the dehazed images. Experimental results on both real and synthetic test data demonstrate that the proposed method performs superiorly against several state-of-the-art land dehazing methods.作者: maintenance 時間: 2025-3-26 17:08 作者: OMIT 時間: 2025-3-27 00:37
0302-9743 , Japan, in November/ December 2020.*.The total of 254 contributions was carefully reviewed and selected from 768 submissions during two rounds of reviewing and improvement. The papers focus on the following topics:..Part I: 3D computer vision; segmentation and grouping..Part II: low-level vision, i作者: ANTIC 時間: 2025-3-27 01:32 作者: 熱烈的歡迎 時間: 2025-3-27 07:10
https://doi.org/10.1007/978-3-319-26047-1iate layers. In this way, GFFRB can enjoy the merits of the lightweight of the group convolution and the high-efficiency of the skip connections, thus achieving better performance compared with most current residual blocks. Experiments on the benchmark test sets show that our models are more efficient than most of the state-of-the-art methods.作者: myalgia 時間: 2025-3-27 11:28
Accurate and Efficient Single Image Super-Resolution with Matrix Channel Attention NetworkCAB). Several models of different sizes are released to meet various practical requirements. Extensive benchmark experiments show that the proposed models achieve better performance with much fewer multiply-adds and parameters (Source code is at .).作者: BORE 時間: 2025-3-27 17:19
An Efficient Group Feature Fusion Residual Network for Image Super-Resolutioniate layers. In this way, GFFRB can enjoy the merits of the lightweight of the group convolution and the high-efficiency of the skip connections, thus achieving better performance compared with most current residual blocks. Experiments on the benchmark test sets show that our models are more efficient than most of the state-of-the-art methods.作者: 我的巨大 時間: 2025-3-27 18:50 作者: 撤退 時間: 2025-3-27 23:53 作者: Emg827 時間: 2025-3-28 04:48
https://doi.org/10.1007/978-1-349-03555-7ith non-stationary textures remains a challenging task for computer vision. In this paper, a novel approach to image inpainting problem is presented, which adapts exemplar-based methods for deep convolutional neural networks. The concept of . is introduced with the purpose of preserving feature cont作者: 反話 時間: 2025-3-28 07:38 作者: 殘忍 時間: 2025-3-28 10:41 作者: accordance 時間: 2025-3-28 15:28 作者: Astigmatism 時間: 2025-3-28 19:32
https://doi.org/10.1007/978-1-349-03555-7with deep convolutional neural networks (DCNN) have made great progress in this task. However, the conventional DCNN-based deraining methods have struggled to exploit deeper and more complex network architectures for pursuing better performance. This study proposes a novel MCGKT-Net for boosting der作者: Acetaldehyde 時間: 2025-3-29 00:09
https://doi.org/10.1007/978-1-349-03555-7ted by applying a simple degradation operator (e.g., bicubic downsampling) to its high-resolution (HR) counterpart, have limited generalization capability on real-world LR images, whose degradation process is much more complex. Several real-world SISR datasets have been constructed to reduce this ga作者: 群居動物 時間: 2025-3-29 03:59 作者: Cognizance 時間: 2025-3-29 08:22 作者: 不真 時間: 2025-3-29 14:58
https://doi.org/10.1007/978-1-349-03555-7tructing ghosting-free HDR images of dynamic scenes from a set of multi-exposure images is a challenging task, especially with large object motion, disparity, and occlusions, leading to visible artifacts using existing methods. In this paper, we propose a Pyramidal Alignment and Masked merging netwo作者: Liberate 時間: 2025-3-29 17:03
The Dictionary of Even More Diseased Englisho devise a dual camera system using a high spatial resolution (HSR) monochrome camera and another low spatial resolution (LSR) color camera for synthesizing the high-quality color image under low-light illumination conditions. The key problem is how to efficiently learn and fuse cross-camera informa作者: Finasteride 時間: 2025-3-29 20:30
https://doi.org/10.1007/978-1-349-17125-5g tasks. However, because of the difference of statistical characteristics of signal-dependent noise and signal-independent noise, it is hard to model real noise for training and blind real image denoising is still an important challenge problem. In this work we propose a method for blind image deno作者: Custodian 時間: 2025-3-30 03:35
Dominique Guin,Kenneth Ruthven,Luc Trouchea single distortion only may not be suitable for many real-world applications. To deal with such cases, some studies have proposed sequentially combined distortions datasets. Viewing in a different point of combining, we introduce a spatially-heterogeneous distortion dataset in which multiple corrup作者: cliche 時間: 2025-3-30 06:45
Paul Drijvers,Koeno Gravemeijerctor subnetwork with dilated convolution is used to estimate a global color transformation matrix. The introduction of the dilated convolution enhances the ability to aggregate spatial information. Secondly, the local features extractor subnetwork with a light dense block structure is designed to le作者: 導師 時間: 2025-3-30 12:00
https://doi.org/10.1007/978-3-319-26047-1ly to improve their performance by simply increasing the depth of their network. Although this strategy can get promising results, it is inefficient in many real-world scenarios because of the high computational cost. In this paper, we propose an efficient group feature fusion residual network (GFFR作者: 附錄 時間: 2025-3-30 13:26 作者: 黑豹 時間: 2025-3-30 17:52 作者: 多樣 時間: 2025-3-30 22:49
https://doi.org/10.1007/978-3-662-65102-5their practical applicability. In this paper, we develop a computation efficient yet accurate network based on the proposed attentive auxiliary features (A.F) for SISR. Firstly, to explore the features from the bottom layers, the auxiliary feature from all the previous layers are projected into a co作者: 莊嚴 時間: 2025-3-31 04:44
Integrating the Engine in the Vehicle,he investigation on rain removal has thus been attracting, while the performances of existing deraining have limitations owing to over smoothing effect, poor generalization capability and rain intensity varies both in spatial locations and color channels. To address these issues, we proposed a Multi作者: 使隔離 時間: 2025-3-31 05:40
Computer Vision – ACCV 2020978-3-030-69532-3Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Detonate 時間: 2025-3-31 12:16
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/234127.jpg作者: 起皺紋 時間: 2025-3-31 15:04
Second-Order Camera-Aware Color Transformation for Cross-Domain Person Re-identification all the views of both source and target domain data with original ImageNet data statistics. This new input normalization method, as shown in our experiments, is much more efficient than simply using ImageNet statistics. We test our method on Market1501, DukeMTMC, and MSMT17 and achieve leading perf作者: laparoscopy 時間: 2025-3-31 18:16
MCGKT-Net: Multi-level Context Gating Knowledge Transfer Network for Single Image Derainingg the knowledge already learned in other task domains. Furthermore, to dynamically select useful features in learning procedure, we propose a multi-scale context gating module in the MCGKT-Net using squeeze-and-excitation block. Experiments on three benchmark datasets: Rain100H, Rain100L, and Rain80作者: 抵消 時間: 2025-3-31 23:17
Degradation Model Learning for Real-World Single Image Super-Resolutionradation kernel as the weighted combination of the basis kernels. With the learned degradation model, a large number of realistic HR-LR pairs can be easily generated to train a more robust SISR model. Extensive experiments are performed to quantitatively and qualitatively validate the proposed degra作者: myriad 時間: 2025-4-1 02:51 作者: 枕墊 時間: 2025-4-1 08:36
Raw-Guided Enhancing Reprocess of Low-Light Image via Deep Exposure Adjustmentsub-modules for unprocessing, reconstruction and processing, respectively. To the best of our knowledge, the proposed sub-net for unprocessing is the first learning-based unprocessing method. After the joint training of three parts, each pre-trained seperately with the RAW image guidance, experiment作者: assail 時間: 2025-4-1 12:19
Robust High Dynamic Range (HDR) Imaging with Complex Motion and Parallaxitly. To make full use of aligned features, we use dilated dense residual blocks with squeeze-and-excitation (SE) attention. Such attention mechanism effectively helps to remove redundant information and suppress misaligned features. Additional mask-based weighting is further employed to refine the
