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- HVI: A New Color Space for Low-light Image Enhancement244
A new color space for LLIE is proposed, namely Horizontal/Vertical-Intensity (HVI), defined by polarized HS maps and learnable intensity, defined by polarized HS maps and learnable intensity.
- NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results85
This paper meticulously evaluates the state-of-the-art advancements in enhancing low-light images, reflecting the significant progress and creativity in this field.
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- ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution73
This research revisits diffusion models in image super-resolution and proposes a straightforward yet significant diffusion model-based super-resolution method called ACDMSR (accelerated conditional diffusion model for image super-resolution), which surpasses previous attempts in qualitative and quantitative results.
- High dynamic range imaging by sparse representation72
A ghost-free HDR image synthesis algorithm that utilizes a sparse representation framework based on the dependency among adjacent low dynamic range images and the sparsity of the moving object that leads to the ghost artifacts is proposed.
- GoMVS: Geometrically Consistent Cost Aggregation for Multi-View Stereo53
GoMVS is proposed to aggregate geometrically consis-tent costs, yielding better utilization of adjacent geometries by leveraging the local geomet-ric smoothness in conjunction with surface normals by the geometric consistent propagation (GCP) module.
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- Learning to Fuse Monocular and Multi-view Cues for Multi-frame Depth Estimation in Dynamic Scenes53
A novel method to learn to fuse the multi-view and monocular cues encoded as volumes without needing the heuristically crafted masks is proposed, which includes the cross-cue attention (CCA) to encode the spatially non-local relative intra-relations from each source to enhance the representation of the other.
- Learning depth via leveraging semantics: Self-supervised monocular depth estimation with both implicit and explicit semantic guidance50
A Semantic-aware Spatial Feature Alignment (SSFA) scheme to effectively align implicit semantic features with depth features for scene-aware depth estimation and a semantic-guided ranking loss to explicitly constrain the estimated depth maps to be consistent with real scene contextual properties are proposed.
- A Unified HDR Imaging Method with Pixel and Patch Level50
A hybrid HDR deghosting network, called HyHDRNet, is proposed to learn the complicated relationship between reference and non-reference images and outperforms state-of-the-art methods both quantitatively and qualitatively, achieving appealing HDR visualization with unified textures and colors.
- Attention-Based Network For Low-Light Image Enhancement48
A novel attention-based neural network to generate high-quality enhanced low-light images from the raw sensor data and proposes a new pooling layer, called inverted shuffle layer, which adaptively selects useful information from previous features.
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- All-in-one Multi-degradation Image Restoration Network via Hierarchical Degradation Representation42
A novel All-in-one Multi-degradation Image Restoration Network (AMIRNet) that can effectively capture and utilize accurate degradation representation for image restoration and design a feature transform block (FTB) that aligns domains and refines features with the guidance of the degradation representation.
- High dynamic range imaging via gradient-aware context aggregation network38
The proposed HDR imaging approach that aggregates the information from multiple LDR images with guidance from image gradient domain generates artifact-free images by integrating the image gradient information and the image context information in the pixel domain.
- Going the Extra Mile in Face Image Quality Assessment: A Novel Database and Model34
The largest annotated IQA database developed to date is introduced, which contains 20,000 human faces – an order of magnitude larger than all existing rated datasets of faces – of diverse individuals in highly varied circumstances and a novel deep learning model is proposed to accurately predict face image quality.
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- Sparse2DGS: Geometry-Prioritized Gaussian Splatting for Surface Reconstruction from Sparse Views30
This work proposes Sparse2DGS, an MVS-initialized Gaussian Splatting pipeline for complete and accurate reconstruction, which outperforms existing methods by notable margins while being 2× faster than the NeRF-based fine-tuning approach.
- Ghost Removal via Channel Attention in Exposure Fusion29
A novel Multi-scale Channel Attention guided Network (MCANet) is proposed to address the ghosting problem by using multi-scale blocks consisting of dilated convolution layers to extract informative features.
- Dim small target detection based on convolutinal neural network in star image27
A fully convolutional neural network is proposed to achieve pixel-wise classification, which can complete target-background separation in a single stage rapidly and outperforms the state-of-the-art in terms of higher detection rate and less false alarm caused by noises.
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- Boosting HDR Image Reconstruction via Semantic Knowledge Transfer25
This framework introduces the Semantic Priors Guided Reconstruction Model (SPGRM), which leverages SDR image semantic knowledge to address ill-posed problems in the initial HDR reconstruction results, and leverages a self-distillation mechanism that constrains the color and content information with semantic knowledge, aligning the external outputs between the baseline and SPGRM.
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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