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- Radu TimofteSuggested from co-authorship
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Works75 from public data
- InstructIR: High-Quality Image Restoration Following Human Instructions234
This work presents the first approach that uses human-written instructions to guide the image restoration model, InstructIR, which can recover high-quality images from their degraded counterparts, considering multiple degradation types.
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- DarkIR: Robust Low-Light Image Restoration141
The model, DarkIR, achieves new state-of-the-art results on the popular LOL-Blur, LOLv2 and Real-LOLBlur datasets, being able to generalize on real-world night and dark images.
- CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification125
This work uses CLIP (Contrastive Language-Image Pre-Training) for training a neural network on a variety of art images and text pairs, being able to learn directly from raw descriptions about images, or if available, curated labels, with zero-shot capability.
- 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.
- NTIRE 2022 Challenge on Perceptual Image Quality Assessment84
This paper reports on the NTIRE 2022 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2022 to address the emerging challenge of IQA by perceptual image processing algorithms.
- Model-Based Image Signal Processors via Learnable Dictionaries65
This work proposes an invertible model, capable of bidirectional mapping between RAW and RGB domains, that employs end-to-end learning of rich parameter representations that are free from direct parametric supervision and additionally enable simple and plausible data augmentation.
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- NTIRE 2024 Quality Assessment of AI-Generated Content Challenge54
This challenge is to address a major challenge in the field of image and video processing, namely, Image Quality Assessment (IQA) and Video Quality Assessment (VQA) for AI-Generated Content (AIGC).
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- Perceptual Image Enhancement for Smartphone Real-Time Applications40
This paper proposes LPIENet, a lightweight network for perceptual image enhancement, with the focus on deploying it on smartphones, and shows that, with much fewer parameters and operations, the model can deal with the mentioned artifacts and achieve competitive performance compared with state-of-the-art methods on standard benchmarks.
- The Tenth NTIRE 2025 Efficient Super-Resolution Challenge Report39
This paper presents a comprehensive review of the NTIRE 2025 Challenge on Single-Image Efficient Super-Resolution, emphasizing groundbreaking advancements in state-of-the-art single-image ESR techniques.
- Event-Based Eye Tracking. AIS 2024 Challenge Survey38
This survey reviews the AIS 2024 Event-Based Eye Tracking (EET) Challenge and examines novel and diverse methods from the submitted factsheets to advance future event-based eye tracking research.
- NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results37
This paper reviews the NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images to establish a new and powerful benchmark for the task of removing raindrops under varying lighting and focus conditions.
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- NTIRE 2025 Challenge on Efficient Burst HDR and Restoration: Datasets, Methods, and Results30
A comprehensive overview of the NTIRE 2025 Efficient Burst HDR and Restoration Challenge is provided, the proposed solutions are compared, and it serves as a valuable reference for researchers and practitioners in efficient burst HDR and restoration.
- NTIRE 2025 Challenge on Low Light Image Enhancement: Methods and Results29
This paper thoroughly evaluates the state-of-the-art advancements in LLIE, showcasing the significant progress.
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- Raw Image Reconstruction From Rgb on Smartphones. Ntire 2025 Challenge Report23
This paper aims to recover RAW sensor images from smartphones given the corresponding sRGB images without metadata and, by doing this, “reverse” the ISP transformation.
- NTIRE 2025 Challenge on Image Super-Resolution ($\times 4$): Methods and Results23
The NTIRE 2025 image super-resolution ($\times 4$) challenge, one of the associated competitions of the 10th NTIRE Workshop at CVPR 2025, aims to recover high-resolution images from low-resolution counterparts generated through bicubic downsampling with a $\times 4$ scaling factor.
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- NTIRE 2026 The Second Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results21
An overview of the NTIRE 2026 Second Challenge on Day and Night Raindrop Removal for Dual-Focused Images demonstrates the growing progress in this challenging task.
- Deep RAW Image Super-Resolution. A NTIRE 2024 Challenge Survey21
The NTIRE 2024 RAW Image Super-Resolution Challenge is reviewed, highlighting the proposed solutions and results, and providing a gauge for the current state-of-the-art in RAW Image Super-Resolution.
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- Deep Portrait Quality Assessment. A NTIRE 2024 Challenge Survey20
The NTIRE 2024 Portrait Quality Assessment Challenge is reviewed, highlighting the proposed solutions and results, and providing a gauge for the current state-of-the-art in Portrait Quality Assessment.
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- Bokehlicious: Photorealistic Bokeh Rendering with Controllable Apertures15
This work proposes Bokehlicious, a highly efficient network that provides intuitive control over Bokeh strength through an ApertureAware Attention mechanism, mimicking the physical lens aperture, achieving competitive results on the RealDOF benchmark.
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- AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and Results13
This paper reviews the AIS 2024 Video Quality Assessment (VQA) Challenge, focused on User-Generated Content (UGC), to gather deep learning-based methods capable of estimating the perceptual quality of UGC videos.
- BSRAW: Improving Blind RAW Image Super-Resolution13
This work designs a realistic degradation pipeline tailored specifically for training models with raw sensor data that can upscale real-scene RAW images and improve their quality, and presents a new DSLM dataset and benchmark for this task.
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- Toward Efficient Deep Blind Raw Image Restoration8
A new realistic degradation pipeline for training deep blind RAW restoration models that considers realistic sensor noise, motion blur, camera shake, and other common degradations and is considered to be the most exhaustive analysis on RAW image restoration.
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- INRetouch: Context Aware Implicit Neural Representation for Photography Retouching4
A novel retouch transfer approach that learns from professional edits through before-after image pairs, enabling precise replication of complex editing operations and a context-aware Implicit Neural Representation that learns to apply edits adaptively based on image content and context.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. 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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