作者: DAMN 時間: 2025-3-21 20:27 作者: Stress 時間: 2025-3-22 02:47 作者: Polydipsia 時間: 2025-3-22 07:47
Toward the End of the Soviet-Type Economyically, we propose a novel Graph-based Self-Tuning (GST) network to reason uncertainties adapting to varying spatial structures of masks among different hardware. Moreover, we develop a bilevel optimization framework to balance HSI reconstruction and uncertainty estimation, accounting for the hyperp作者: originality 時間: 2025-3-22 09:02 作者: AVID 時間: 2025-3-22 14:22 作者: AVID 時間: 2025-3-22 17:45
Performance of the Soviet-Type Economyn (SFI) block in two formats tailored to these two sub-networks, which interactively process the local spatial features and the global frequency information to encourage the complementary learning. Extensive experiments demonstrate that our method achieves superior results than other approaches with作者: cinder 時間: 2025-3-22 21:33
The Economics of Property Rightsal domain. Extensive experiments on synthetic and real-world datasets demonstrate that our method outperforms the state-of-the-art approaches both visually and quantitatively. Our code is released publicly at ..作者: 獎牌 時間: 2025-3-23 05:20 作者: 公社 時間: 2025-3-23 08:58 作者: 特別容易碎 時間: 2025-3-23 13:41 作者: 刻苦讀書 時間: 2025-3-23 17:54 作者: hankering 時間: 2025-3-23 19:50
https://doi.org/10.1007/b102393bi-directional hole filling techniques to alleviate the artifacts of the image synthesis. In the E-step, RIPR renders new images to create a large quantity of training data. In the M-step, we utilize the generated training data to train an optical flow network, which can be used to estimate optical 作者: Boycott 時間: 2025-3-24 01:13 作者: GULF 時間: 2025-3-24 05:14 作者: Obsequious 時間: 2025-3-24 09:15
,Optimizing Image Compression via?Joint Learning with?Denoising,lug-in feature denoisers to allow a simple and effective realization of the goal with little computational cost. Experimental results show that our method gains a significant improvement over the existing baseline methods on both the synthetic and real-world datasets. Our source code is available at作者: 褪色 時間: 2025-3-24 14:33
,Restore Globally, Refine Locally: A Mask-Guided Scheme to?Accelerate Super-Resolution Networks,e select . feature patches from the coarse feature and refine them (instead of the whole feature) by Refine-Net to output the final SR image. Experiments on seven benchmarks demonstrate that our MGA scheme reduces the FLOPs of five popular SR networks by 10%–48% with comparable or even better SR per作者: Yourself 時間: 2025-3-24 18:29 作者: inculpate 時間: 2025-3-24 19:09
,Modeling Mask Uncertainty in?Hyperspectral Image Reconstruction,ically, we propose a novel Graph-based Self-Tuning (GST) network to reason uncertainties adapting to varying spatial structures of masks among different hardware. Moreover, we develop a bilevel optimization framework to balance HSI reconstruction and uncertainty estimation, accounting for the hyperp作者: 雜色 時間: 2025-3-24 23:29 作者: ODIUM 時間: 2025-3-25 06:19 作者: 落葉劑 時間: 2025-3-25 11:21
,Deep Fourier-Based Exposure Correction Network with?Spatial-Frequency Interaction,n (SFI) block in two formats tailored to these two sub-networks, which interactively process the local spatial features and the global frequency information to encourage the complementary learning. Extensive experiments demonstrate that our method achieves superior results than other approaches with作者: 藐視 時間: 2025-3-25 14:01
,Frequency and?Spatial Dual Guidance for?Image Dehazing,al domain. Extensive experiments on synthetic and real-world datasets demonstrate that our method outperforms the state-of-the-art approaches both visually and quantitatively. Our code is released publicly at ..作者: Hallowed 時間: 2025-3-25 16:49
,Learning Discriminative Shrinkage Deep Networks for?Image Deconvolution,rties of the Maxout function and develop a deep CNN model with Maxout layers to learn discriminative shrinkage functions, which directly approximates the solutions of these two sub-problems. Moreover, the fast-Fourier-transform-based image restoration usually leads to ringing artifacts. At the same 作者: 愛好 時間: 2025-3-25 23:05
,KXNet: A Model-Driven Deep Neural Network for?Blind Super-Resolution,ear physical patterns and the mutually iterative process between blur kernel and HR image can soundly guide the KXNet to be evolved in the right direction. Extensive experiments on synthetic and real data finely demonstrate the superior accuracy and generality of our method beyond the current repres作者: hermetic 時間: 2025-3-26 02:20
ARM: Any-Time Super-Resolution Method, computation-performance tradeoff. Moreover, each SISR subnet shares weights of the ARM supernet, thus no extra parameters are introduced. The setting of multiple subnets can well adapt the computational cost of SISR model to the dynamically available hardware resources, allowing the SISR task to be作者: venous-leak 時間: 2025-3-26 05:01 作者: DUST 時間: 2025-3-26 12:25
,RealFlow: EM-Based Realistic Optical Flow Dataset Generation from?Videos,bi-directional hole filling techniques to alleviate the artifacts of the image synthesis. In the E-step, RIPR renders new images to create a large quantity of training data. In the M-step, we utilize the generated training data to train an optical flow network, which can be used to estimate optical 作者: geometrician 時間: 2025-3-26 15:33 作者: 使更活躍 時間: 2025-3-26 18:02 作者: minaret 時間: 2025-3-26 23:55
Conference proceedings 2022ning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation..作者: Rustproof 時間: 2025-3-27 01:36
0302-9743 ruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation..978-3-031-19799-4978-3-031-19800-7Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 迷住 時間: 2025-3-27 08:49 作者: Genetics 時間: 2025-3-27 10:16
The Finances of Professional Cycling Teamsent mode to increase transferability. The searched architecture on the CAVE dataset has been adopted for various reconstruction tasks, and achieves remarkable performance. On the basis of fruitful experiments, we conclude that the transferability of searched architecture is dependent on the spectral information and independent of the noise levels.作者: Herbivorous 時間: 2025-3-27 16:50 作者: Indicative 時間: 2025-3-27 20:24
The Right of Ownership and the Firmhort-term memory unit in the image and feature spaces. With this method, both the interpretability and representation ability of the deep network are improved. Extensive experiments demonstrate the superiority of our method to the existing state-of-the-art approaches. The source code is released at ..作者: 整頓 時間: 2025-3-28 01:50
,Learning Mutual Modulation for?Self-supervised Cross-Modal Super-Resolution,esolution of the guide and induce the guide to mimic the modality characteristics of the source. Moreover, we adopt a cycle consistency constraint to train MMSR in a fully self-supervised manner. Experiments on various tasks demonstrate the state-of-the-art performance of our MMSR.作者: BOAST 時間: 2025-3-28 05:26 作者: DAFT 時間: 2025-3-28 08:13 作者: harangue 時間: 2025-3-28 12:46
,Memory-Augmented Model-Driven Network for?Pansharpening,hort-term memory unit in the image and feature spaces. With this method, both the interpretability and representation ability of the deep network are improved. Extensive experiments demonstrate the superiority of our method to the existing state-of-the-art approaches. The source code is released at ..作者: Legend 時間: 2025-3-28 17:29 作者: GUILE 時間: 2025-3-28 22:20
,Learning Mutual Modulation for?Self-supervised Cross-Modal Super-Resolution,esolution (LR) source and high-resolution (HR) guide images from different modalities are available. Existing methods utilize pseudo or weak supervision in LR space and thus deliver results that are blurry or not faithful to the source modality. To address this issue, we present a mutual modulation 作者: 有說服力 時間: 2025-3-29 01:07 作者: 誹謗 時間: 2025-3-29 04:29
,Neural Color Operators for?Sequential Image Retouching,or operator mimics the behavior of traditional color operators and learns pixelwise color transformation while its strength is controlled by a scalar. To reflect the homomorphism property of color operators, we employ equivariant mapping and adopt an encoder-decoder structure which maps the non-line作者: magenta 時間: 2025-3-29 08:46
,Optimizing Image Compression via?Joint Learning with?Denoising,brings extra challenges to lossy image compression algorithms. Without the capacity to tell the difference between image details and noise, general image compression methods allocate additional bits to explicitly store the undesired image noise during compression and restore the unpleasant noisy ima作者: ACME 時間: 2025-3-29 14:03 作者: Anal-Canal 時間: 2025-3-29 17:10
,Compiler-Aware Neural Architecture Search for?On-Mobile Real-time Super-Resolution,ication scenarios. However, prior methods typically suffer from large amounts of computations and huge power consumption, causing difficulties for real-time inference, especially on resource-limited platforms such as mobile devices. To mitigate this, we propose a compiler-aware SR neural architectur作者: Prologue 時間: 2025-3-29 20:25
,Modeling Mask Uncertainty in?Hyperspectral Image Reconstruction, imaging (CASSI) system. Existing deep HSI reconstruction models are generally trained on paired data to retrieve original signals upon 2D compressed measurements given by a particular optical hardware mask in CASSI, during which the mask largely impacts the reconstruction performance and could work作者: 帳單 時間: 2025-3-30 02:35 作者: 外形 時間: 2025-3-30 04:39
,Stripformer: Strip Transformer for?Fast Image Deblurring,egion-specific smoothing artifacts that are often directional and non-uniform, which is difficult to be removed. Inspired by the current success of transformers on computer vision and image processing tasks, we develop, Stripformer, a transformer-based architecture that constructs intra- and inter-s作者: 碎片 時間: 2025-3-30 08:14 作者: pineal-gland 時間: 2025-3-30 15:00
,Frequency and?Spatial Dual Guidance for?Image Dehazing,image dehazing methods that primarily exploit the spatial information and neglect the distinguished frequency information, we introduce a new perspective to address image dehazing by jointly exploring the information in the frequency and spatial domains. To implement frequency and spatial dual guida作者: Perineum 時間: 2025-3-30 19:56 作者: 使聲音降低 時間: 2025-3-30 22:01 作者: champaign 時間: 2025-3-31 02:45
ARM: Any-Time Super-Resolution Method, motivated by three observations: (1) The performance of different image patches varies with SISR networks of different sizes. (2) There is a tradeoff between computation overhead and performance of the reconstructed image. (3) Given an input image, its edge information can be an effective option to作者: 勉強 時間: 2025-3-31 06:39
,Attention-Aware Learning for?Hyperparameter Prediction in?Image Processing Pipelines,ignal and feed it into downstream tasks. The processing blocks in ISPs depend on a set of tunable hyperparameters that have a complex interaction with the output. Manual setting by image experts is the traditional way of hyperparameter tuning, which is time-consuming and biased towards human percept作者: SLAG 時間: 2025-3-31 12:57 作者: 燦爛 時間: 2025-3-31 14:15
,Memory-Augmented Model-Driven Network for?Pansharpening,timation (MAP) model with two well-designed priors on the latent multi-spectral (MS) image, i.e., global and local implicit priors to explore the intrinsic knowledge across the modalities of MS and panchromatic (PAN) images. Second, we design an effective alternating minimization algorithm to solve 作者: RAGE 時間: 2025-3-31 19:30
,All You Need Is RAW: Defending Against Adversarial Attacks with?Camera Image Pipelines,ool these models into making a false prediction on an image that was correctly predicted without the perturbation. Various defense methods have proposed image-to-image mapping methods, either including these perturbations in the training process or removing them in a preprocessing step. In doing so,作者: AXIOM 時間: 2025-3-31 21:41 作者: 愛管閑事 時間: 2025-4-1 03:15
The Finances of Professional Cycling Teamsnformation of HSIs still remains a challenge. In this work, we disentangle the 3D convolution into lightweight 2D spatial and spectral convolutions, and build a spectrum-aware search space for HSI restoration. Subsequently, we utilize neural architecture search strategy to automatically learn the mo作者: 搖曳 時間: 2025-4-1 06:19
The History of Professional Road Cycling,or operator mimics the behavior of traditional color operators and learns pixelwise color transformation while its strength is controlled by a scalar. To reflect the homomorphism property of color operators, we employ equivariant mapping and adopt an encoder-decoder structure which maps the non-line
