作者: 顧客 時間: 2025-3-21 21:03 作者: 暫停,間歇 時間: 2025-3-22 03:28
Jeyakrishna Velauthapillai,Johannes Flo?-HD images..The extensive experiments on three large-scale image desnowing datasets demonstrate that our method surpasses all state-of-the-art approaches by a large margin both quantitatively and qualitatively, boosting the PSNR metric from 31.76 dB to 34.10 dB on the CSD test dataset and from 28.29作者: Synapse 時間: 2025-3-22 07:49
Jens Freche,Milan den Heijer,Bastian Wormuthation performance caused by training on synthetic datasets. Quantitative and qualitative experiments show that the proposed method significantly outperforms state-of-the-art methods on real-world cloud images. The source code and dataset are available at ..作者: 較早 時間: 2025-3-22 11:37 作者: 精密 時間: 2025-3-22 14:14 作者: 精密 時間: 2025-3-22 17:57 作者: 不可救藥 時間: 2025-3-22 22:42
From Textualism to Hypertextualism’s density and uneven distribution. Based on the uncertainty map, our feedback network refines our defogged output iteratively. Moreover, to handle the intractability of estimating the atmospheric light colors, we exploit the grayscale version of our input image, since it is less affected by varying作者: Fissure 時間: 2025-3-23 01:37 作者: annexation 時間: 2025-3-23 07:11 作者: 有危險 時間: 2025-3-23 11:08 作者: 強化 時間: 2025-3-23 16:58 作者: 褪色 時間: 2025-3-23 21:07 作者: Lucubrate 時間: 2025-3-23 22:32
https://doi.org/10.1007/978-3-030-88221-1MatchFormer is a multi-win solution in efficiency, robustness, and precision. Compared to the previous best method in indoor pose estimation, our lite MatchFormer has only . GFLOPs, yet achieves a . precision gain and a . running speed boost. The large MatchFormer reaches state-of-the-art on four di作者: 豐滿中國 時間: 2025-3-24 06:20 作者: Yourself 時間: 2025-3-24 06:36
Modular Degradation Simulation and?Restoration for?Under-Display Camera-style network named DWFormer for UDC image restoration. For practical purposes, we use depth-wise convolution instead of the multi-head self-attention to aggregate local spatial information. Moreover, we propose a novel channel attention module to aggregate global information, which is critical for作者: 天然熱噴泉 時間: 2025-3-24 12:25
UHD Underwater Image Enhancement via?Frequency-Spatial Domain Aware Networkd branch, we develop U-RSGNet to capture the color features of the image after Gaussian blurring to generate a feature map rich in color information. Finally, the extracted texture features are fused with the color features to produce a clear underwater image. In addition, to construct paired high-q作者: 拘留 時間: 2025-3-24 17:07 作者: 喧鬧 時間: 2025-3-24 20:16
Uncertainty-Based Thin Cloud Removal Network via?Conditional Variational Autoencodersation performance caused by training on synthetic datasets. Quantitative and qualitative experiments show that the proposed method significantly outperforms state-of-the-art methods on real-world cloud images. The source code and dataset are available at ..作者: Diatribe 時間: 2025-3-24 23:54 作者: 騎師 時間: 2025-3-25 07:18 作者: Anthropoid 時間: 2025-3-25 08:50
Multi-granularity Transformer for?Image Super-Resolutionntly aggregate both local and global information for accurate reconstruction. Extensive experiments on five benchmark datasets demonstrate that our MugFormer performs favorably against state-of-the-art methods in terms of both quantitative and qualitative results.作者: 呼吸 時間: 2025-3-25 15:11 作者: 高度表 時間: 2025-3-25 19:15
DualBLN: Dual Branch LUT-Aware Network for?Real-Time Image Retouchingwe employ bilinear pooling to solve the problem of feature information loss that occurs when fusing features from the dual branch network, avoiding the feature distortion caused by direct concatenation or summation. Extensive experiments on several datasets demonstrate the effectiveness of our work,作者: 貴族 時間: 2025-3-25 20:46
CSIE: Coded Strip-Patterns Image Enhancement Embedded in?Structured Light-Based MethodsIE results can be achieved accordingly and further improve the details performance of 3D model reconstruction. Experiments on multiple sets of challenging CSI sequences show that our CSIE outperforms the existing used for natural image-enhanced methods in terms of 2D enhancement, point clouds extrac作者: 潰爛 時間: 2025-3-26 03:11 作者: Indolent 時間: 2025-3-26 04:58 作者: PIZZA 時間: 2025-3-26 11:07 作者: 瑪瑙 時間: 2025-3-26 15:30
MatchFormer: Interleaving Attention in?Transformers for?Feature MatchingMatchFormer is a multi-win solution in efficiency, robustness, and precision. Compared to the previous best method in indoor pose estimation, our lite MatchFormer has only . GFLOPs, yet achieves a . precision gain and a . running speed boost. The large MatchFormer reaches state-of-the-art on four di作者: gentle 時間: 2025-3-26 20:06 作者: 癡呆 時間: 2025-3-26 22:29
0302-9743 art VII: generative models for computer vision; segmentation and grouping; motion and tracking; document image analysis; big data, large scale methods. .978-3-031-26312-5978-3-031-26313-2Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 易彎曲 時間: 2025-3-27 01:40 作者: 向外才掩飾 時間: 2025-3-27 07:58 作者: incision 時間: 2025-3-27 09:46
Dirk Slama,Tanja Rückert,Heiner LasiSDN, the dehazing performance can be easily finetuned with an additional dataset that can be built by simply collecting hazy images. Experimental results show that our proposed SSDN is lightweight and shows competitive dehazing performance with strong generalization capability over various data domains.作者: endure 時間: 2025-3-27 16:23
Multi-Branch Network with?Ensemble Learning for?Text Removal in?the?Wild a patch attention module to perceive text location and generate text attention features. Our method outperforms state-of-the-art approaches on both real-world and synthetic datasets, improving PSNR by 1.78 dB in the SCUT-EnsText dataset and 4.45 dB in the SCUT-Syn dataset.作者: 可用 時間: 2025-3-27 18:04
Lightweight Alpha Matting Network Using Distillation-Based Channel Pruningtitative and qualitative experiments with in-depth analyses. Furthermore, we demonstrate the versatility of the proposed distillation-based channel pruning method by applying it to semantic segmentation.作者: 最初 時間: 2025-3-27 22:57
Self-Supervised Dehazing Network Using Physical PriorsSDN, the dehazing performance can be easily finetuned with an additional dataset that can be built by simply collecting hazy images. Experimental results show that our proposed SSDN is lightweight and shows competitive dehazing performance with strong generalization capability over various data domains.作者: Gleason-score 時間: 2025-3-28 02:44
Conference proceedings 2023ing, and shape representation; datasets and performance analysis;.Part VI: biomedical image analysis; deep learning for computer vision; ..Part VII: generative models for computer vision; segmentation and grouping; motion and tracking; document image analysis; big data, large scale methods. .作者: dapper 時間: 2025-3-28 09:07 作者: antiquated 時間: 2025-3-28 13:57
0302-9743 China, December 2022...The total of 277 contributions included in the proceedings set was carefully reviewed and selected from 836 submissions during two rounds of reviewing and improvement. The papers focus on the following topics:..Part I: 3D computer vision; optimization methods;.Part II: applic作者: PACK 時間: 2025-3-28 17:46 作者: 你不公正 時間: 2025-3-28 21:59
UHD Underwater Image Enhancement via?Frequency-Spatial Domain Aware Networkderwater exploration. However, due to the poor light transmission in deep water spaces and the large number of impurity particles, UHD underwater imaging is often plagued by low contrast and blur. To overcome these challenges, we propose an efficient two-path model (UHD-SFNet) that recovers the colo作者: 字謎游戲 時間: 2025-3-29 00:17 作者: CHASE 時間: 2025-3-29 06:46 作者: FOIL 時間: 2025-3-29 07:55 作者: perpetual 時間: 2025-3-29 13:56
Multi-Branch Network with?Ensemble Learning for?Text Removal in?the?Wildicacy of background, earlier STR approaches may not successfully remove scene text. We discovered that different networks produce different text removal results. Thus, we present a novel STR approach with a multi-branch network to entirely erase the text while maintaining the integrity of the backgr作者: facetious 時間: 2025-3-29 16:21
Lightweight Alpha Matting Network Using Distillation-Based Channel Pruningdemand for a lightweight alpha matting model due to the limited computational resources of commercial portable devices. To this end, we suggest a distillation-based channel pruning method for the alpha matting networks. In the pruning step, we remove channels of a student network having fewer impact作者: interrupt 時間: 2025-3-29 20:03 作者: Cantankerous 時間: 2025-3-30 00:35 作者: 滴注 時間: 2025-3-30 07:10 作者: Ligament 時間: 2025-3-30 08:49
DualBLN: Dual Branch LUT-Aware Network for?Real-Time Image Retouchingem into a discrete 3D lattice. We propose . (Dual Branch LUT-aware Network) which innovatively incorporates the data representing the color transformation of 3D LUT into the real-time retouching process, which forces the network to learn the adaptive weights and the multiple 3D LUTs with strong repr作者: 豐富 時間: 2025-3-30 15:21
CSIE: Coded Strip-Patterns Image Enhancement Embedded in?Structured Light-Based Methods Besides degrading the visual perception of the CSI, this poor quality also significantly affects the performance of 3D model reconstruction. Most of the existing image-enhanced methods, however, focus on processing natural images but not CSI. In this paper, we propose a novel and effective CSI enha作者: Vulnerary 時間: 2025-3-30 18:21
Teacher-Guided Learning for?Blind Image Quality Assessmentn, as a closely-related task with BIQA, can easily acquire training data without annotation. Moreover, both image semantic and distortion information are vital knowledge for the two tasks to predict and improve image quality. Inspired by these, this paper proposes a novel BIQA framework, which build作者: GAVEL 時間: 2025-3-30 23:27 作者: 過份 時間: 2025-3-31 02:09 作者: Immunoglobulin 時間: 2025-3-31 08:35 作者: Gorilla 時間: 2025-3-31 09:56 作者: 神圣不可 時間: 2025-3-31 13:54
Self-Supervised Dehazing Network Using Physical Priorsimates a clear image, transmission map, and atmospheric airlight out of the input hazy image based on the Atmospheric Scattering Model (ASM). It is trained in a self-supervised manner, utilizing recent self-supervised training methods and physical prior knowledge for obtaining realistic outputs. Tha作者: adjacent 時間: 2025-3-31 17:58
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/234134.jpg
