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Works44 from public data
- 186
- Neural Degradation Representation Learning for All-in-One Image Restoration98
This paper proposes to learn a neural degradation representation (NDR) that captures the underlying characteristics of various degradations that adaptively decomposes different types of degradations, similar to a neural dictionary that represents basic degradation components.
- Exposure-Consistency Representation Learning for Exposure Correction53
A proposed ECP module to consistently learn the representation of both underexposure and overexposure in the feature space and an Exposure-Consistency Constraining strategy that augments the various local region exposures and then constrains the feature representation change between the exposure augmented image and the original one are developed.
- 52
- Towards Bidirectional Arbitrary Image Rescaling: Joint Optimization and Cycle Idempotence42
The proposed method is the first to treat arbitrary rescaling, both upscaling and downscaling, as one unified process, and is able to learn up Scaling and Downscaling simultaneously and achieve bidirectional arbitrary image rescaling.
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- DSANet: Dynamic Segment Aggregation Network for Video-Level Representation Learning35
The proposed DSA module is an efficient plug-and-play module that can be combined with the off-the-shelf clip-based models to perform powerful long-range modeling with minimal overhead and benefit various video recognition models significantly.
- 34
- Mutual-Guided Dynamic Network for Image Fusion33
A novel mutual-guided dynamic network (MGDN) is proposed for image fusion, which allows for effective information utilization across different locations and inputs and outperforms existing methods on four image fusion tasks.
- Spectral-depth imaging with deep learning based reconstruction30
This is the first attempt to capture 5D information (3D space + 1D spectrum +1D time) with a miniaturized apparatus and without active illumination.
- Generalized Lightness Adaptation with Channel Selective Normalization28
A novel generalized lightness adaptation algorithm that extends conventional normalization techniques through a channel filtering design, dubbed Channel Selective Normalization (CSNorm), which purposely normalizes the statistics of lightness-relevant channels and keeps other channels unchanged, so as to improve feature generalization and discrimination.
- PolarFree: Polarization-based Reflection-Free Imaging27
A large-scale dataset, PolaRGB, is constructed for Polarization-based reflection removal of RGB images, which enables us to train models that generalize effectively across a wide range of real-world scenarios and introduces PolarFree, which leverages diffusion process to generate reflection-free cues for accurate reflection removal.
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- SIR-Former: Stereo Image Restoration Using Transformer25
A stereo image restoration transformer (SIR-Former) is constructed to effectively exploit the cross-view correlations and can remarkably boost quantitative and qualitative quality on various image restoration tasks, which demonstrate the effectiveness of the proposed framework.
- Stereo Video Super-Resolution via Exploiting View-Temporal Correlations24
A novel Stereo Video Super-Resolution Network (SVSRNet) is proposed to fulfill the StereoVSR task via exploiting view-temporal correlations to enforce consistency constraint of superresolved stereo videos.
- Structure- and Texture-Aware Learning for Low-Light Image Enhancement21
A Structure and Texture Aware Network (STAN) is proposed for low-light image enhancement, which consists of a structure sub- network and a texture sub-network and a dual distillation scheme that assists the learning process of two sub-networks via counterparts' normal-light structure and texture representations.
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- Continuous Spectral Reconstruction from RGB Images via Implicit Neural Representation16
This paper introduces a novel continuous spectral representation, NeSR, and embraces the concept of implicit function and implements a parameterized embodiment with a neural network to extend the flexibility of spectral reconstruction by enabling an arbitrary number of spectral bands as the target output.
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- Machine Learning Boosted Entropy-Engineered Synthesis of CuCo Nanometric Solid Solution Alloys for Near-100% Nitrate-to-Ammonia Selectivity15
A data-driven approach incorporating sample balancing enhancement techniques and multilayer perceptron (MLP) algorithms to improve the model's ability to handle imbalanced data, significantly boosting the efficiency of experimental parameter optimization is utilized.
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- Continuous Spatial-Spectral Reconstruction via Implicit Neural Representation9
The proposed neural spatial-spectral representation (NeSSR), which projects the deep features extracted from low-resolution inputs to the corresponding intensity values under target 3D coordinates, allows for spectral image reconstruction with arbitrary resolutions in both spatial and spectral dimensions for the first time.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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