MetaEarth-MM unifies multi-modal remote sensing image generation and any-to-any translation across five modalities via scene-centered joint modeling on the new EarthMM dataset.
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Image quality assessment: from error visibility to structural similarity
19 Pith papers cite this work. Polarity classification is still indexing.
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PROVE proposes RC metrics for perceptual removal coherence and releases PROVE-Bench to better align automatic scores with human judgments on object removal tasks.
Rate-constrained minimum entropy coupling enables cross-domain lossy compression with classification preservation, providing closed-form solutions for Bernoulli sources and neural implementations for MNIST and SVHN tasks.
AIR amortizes 2D Gaussian splatting into a self-supervised feed-forward network via residual stages, explicit stage control, and Predict-Optimize-Distill training.
3D Skew Gaussian Splatting extends standard 3D Gaussian Splatting with skew primitives, enhanced opacity, depth-aware densification, and a re-derived CUDA pipeline for a free-camera visualization engine.
The paper proposes the Degradation Frequency Curve (DFC) as an explicit spectral representation for quantifying degradations and develops a DFC-guided multi-scale restorer that achieves state-of-the-art performance on composite and real-world benchmarks.
RDDM introduces a residual drifting field with attractive and repulsive forces to achieve one-step supervised denoising of low-dose CT, reporting superior PSNR, SSIM, FID of 5.87, and 15 ms inference time.
D2-CDIG conditions diffusion models on DEM and cloud-fog priors to generate controlled remote sensing images with decoupled terrain and atmospheric control.
InkDiffuser generates high-fidelity one-shot Chinese calligraphy using high-frequency enhancement and a differentiable ink structure loss for realistic stroke and ink rendering.
StructDiff adds adaptive receptive fields and 3D positional encoding to a single-scale diffusion model to preserve structure and enable spatial control in single-image generation.
DailyArt recovers full joint parameters of articulated objects from a single static image by synthesizing an opened state and comparing discrepancies, supporting downstream part-level novel state synthesis.
MesonGS++ achieves over 34x compression of 3D Gaussian Splatting models post-training while preserving or exceeding original rendering quality through size-aware hyperparameter optimization.
SALD decouples remote sensing images into compressed payload plus structural prior at the edge and uses structure-gated diffusion on the cloud to improve super-resolution and downstream detection under extreme bandwidth limits.
A data-adaptive probabilistic intensity remapping framework using Gaussian POVMs enables continuous, structure-preserving transformations in grayscale images with tunable sharpness and component resolution.
Equivariance2Inverse merges equivariant imaging and sparse reconstruction into a self-supervised CT method that remains effective under scintillator blurring and limited-angle geometries, outperforming prior methods on real 2DeteCT data.
Scene-adaptive lattice vector quantization improves rate-distortion performance of 3DGS compression over uniform scalar quantization while adding little overhead and supporting multiple bit rates from one trained model.
TEA is a new targeted adversarial attack that incorporates edge information from the target image to reduce query count and improve performance in low-query black-box hard-label settings.
ICA-based artifact removal does not consistently improve deep network decoding performance on EEG data across three BCI tasks and multiple models.
Perceptual quality metrics correlate strongly with each other but show minimal correlation with attack success rate across medical imaging models and datasets, making ASR alone inadequate for assessing adversarial robustness.
citing papers explorer
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MetaEarth-MM: Unified Multimodal Remote Sensing Image Generation with Scene-centered Joint Modeling
MetaEarth-MM unifies multi-modal remote sensing image generation and any-to-any translation across five modalities via scene-centered joint modeling on the new EarthMM dataset.
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PROVE: A Perceptual RemOVal cohErence Benchmark for Visual Media
PROVE proposes RC metrics for perceptual removal coherence and releases PROVE-Bench to better align automatic scores with human judgments on object removal tasks.
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Cross-Domain Lossy Compression via Constrained Minimum Entropy Coupling
Rate-constrained minimum entropy coupling enables cross-domain lossy compression with classification preservation, providing closed-form solutions for Bernoulli sources and neural implementations for MNIST and SVHN tasks.
-
AIR: Amortized Image Reconstruction Framework for Self-Supervised Feed-Forward 2D Gaussian Splatting
AIR amortizes 2D Gaussian splatting into a self-supervised feed-forward network via residual stages, explicit stage control, and Predict-Optimize-Distill training.
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3D Skew Gaussian Splatting with Any Camera Trajectory Visualization Engine
3D Skew Gaussian Splatting extends standard 3D Gaussian Splatting with skew primitives, enhanced opacity, depth-aware densification, and a re-derived CUDA pipeline for a free-camera visualization engine.
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Degradation Frequency Curve: An Explicit Frequency-Quantified Representation for All-in-One Image Restoration
The paper proposes the Degradation Frequency Curve (DFC) as an explicit spectral representation for quantifying degradations and develops a DFC-guided multi-scale restorer that achieves state-of-the-art performance on composite and real-world benchmarks.
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RDDM: A Residual-Driven Drifting Model for High-Fidelity Low-Dose CT Denoising
RDDM introduces a residual drifting field with attractive and repulsive forces to achieve one-step supervised denoising of low-dose CT, reporting superior PSNR, SSIM, FID of 5.87, and 15 ms inference time.
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D2-CDIG: Controlled Diffusion Remote Sensing Image Generation with Dual Priors of DEM and Cloud-Fog
D2-CDIG conditions diffusion models on DEM and cloud-fog priors to generate controlled remote sensing images with decoupled terrain and atmospheric control.
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InkDiffuser: High-Fidelity One-shot Chinese Calligraphy via Differentiable Morphological Optimization
InkDiffuser generates high-fidelity one-shot Chinese calligraphy using high-frequency enhancement and a differentiable ink structure loss for realistic stroke and ink rendering.
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StructDiff: A Structure-Preserving and Spatially Controllable Diffusion Model for Single-Image Generation
StructDiff adds adaptive receptive fields and 3D positional encoding to a single-scale diffusion model to preserve structure and enable spatial control in single-image generation.
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DailyArt: Discovering Articulation from Single Static Images via Latent Dynamics
DailyArt recovers full joint parameters of articulated objects from a single static image by synthesizing an opened state and comparing discrepancies, supporting downstream part-level novel state synthesis.
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MesonGS++: Post-training Compression of 3D Gaussian Splatting with Hyperparameter Searching
MesonGS++ achieves over 34x compression of 3D Gaussian Splatting models post-training while preserving or exceeding original rendering quality through size-aware hyperparameter optimization.
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Edge-Cloud Collaborative Reconstruction via Structure-Aware Latent Diffusion for Downstream Remote Sensing Perception
SALD decouples remote sensing images into compressed payload plus structural prior at the edge and uses structure-gated diffusion on the cloud to improve super-resolution and downstream detection under extreme bandwidth limits.
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Unsharp Measurement with Adaptive Gaussian POVMs for Quantum-Inspired Image Processing
A data-adaptive probabilistic intensity remapping framework using Gaussian POVMs enables continuous, structure-preserving transformations in grayscale images with tunable sharpness and component resolution.
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Equivariance2Inverse: A Practical Self-Supervised CT Reconstruction Method Benchmarked on Real, Limited-Angle, and Blurred Data
Equivariance2Inverse merges equivariant imaging and sparse reconstruction into a self-supervised CT method that remains effective under scintillator blurring and limited-angle geometries, outperforming prior methods on real 2DeteCT data.
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Improving 3D Gaussian Splatting Compression by Scene-Adaptive Lattice Vector Quantization
Scene-adaptive lattice vector quantization improves rate-distortion performance of 3DGS compression over uniform scalar quantization while adding little overhead and supporting multiple bit rates from one trained model.
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Accelerating Targeted Hard-Label Adversarial Attacks in Low-Query Black-Box Settings
TEA is a new targeted adversarial attack that incorporates edge information from the target image to reduce query count and improve performance in low-query black-box hard-label settings.
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I see artifacts: ICA-based EEG artifact removal does not improve deep network decoding across three BCI tasks
ICA-based artifact removal does not consistently improve deep network decoding performance on EEG data across three BCI tasks and multiple models.
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Beyond Attack Success Rate: A Multi-Metric Evaluation of Adversarial Transferability in Medical Imaging Models
Perceptual quality metrics correlate strongly with each other but show minimal correlation with attack success rate across medical imaging models and datasets, making ASR alone inadequate for assessing adversarial robustness.