102 citations · 234 across the 46 of their papers we have counts for
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NTIRE 2025 Challenge on RAW Image Restoration and Super-Resolution
Marcos V. Conde, Radu Timofte, Zihao Lu +36
This paper reviews the NTIRE 2025 RAW Image Restoration and Super-Resolution Challenge, highlighting the proposed solutions and results. New methods for RAW Restoration and Super-R…
RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report
Marcos V. Conde, Radu Timofte, Radu Berdan +30
Numerous low-level vision tasks operate in the RAW domain due to its linear properties, bit depth, and sensor designs. Despite this, RAW image datasets are scarce and more expensiv…
INRetouch: Context Aware Implicit Neural Representation for Photography Retouching
Omar Elezabi, Marcos V. Conde, Zongwei Wu +1
Professional photo editing remains challenging, requiring extensive knowledge of imaging pipelines and significant expertise. While recent deep learning approaches, particularly st…
Toward Efficient Deep Blind RAW Image Restoration
Marcos V. Conde, Florin Vasluianu, Radu Timofte
Multiple low-vision tasks such as denoising, deblurring and super-resolution depart from RGB images and further reduce the degradations, improving the quality. However, modeling th…
AIM 2024 Challenge on Efficient Video Super-Resolution for AV1 Compressed Content
Marcos V Conde, Zhijun Lei, Wen Li +3
Video super-resolution (VSR) is a critical task for enhancing low-bitrate and low-resolution videos, particularly in streaming applications. While numerous solutions have been deve…
Compressed Depth Map Super-Resolution and Restoration: AIM 2024 Challenge Results
Marcos V. Conde, Florin-Alexandru Vasluianu, Jinhui Xiong +3
The increasing demand for augmented reality (AR) and virtual reality (VR) applications highlights the need for efficient depth information processing. Depth maps, essential for ren…
BSRAW: Improving Blind RAW Image Super-Resolution
Marcos V. Conde, Florin Vasluianu, Radu Timofte
In smartphones and compact cameras, the Image Signal Processor (ISP) transforms the RAW sensor image into a human-readable sRGB image. Most popular super-resolution methods depart…
Real-Time Under-Display Cameras Image Restoration and HDR on Mobile Devices
Marcos V. Conde, Florin Vasluianu, Sabari Nathan +1
The new trend of full-screen devices implies positioning the camera behind the screen to bring a larger display-to-body ratio, enhance eye contact, and provide a notch-free viewing…
Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report
Marcos V. Conde, Radu Timofte, Yibin Huang +40
Cameras capture sensor RAW images and transform them into pleasant RGB images, suitable for the human eyes, using their integrated Image Signal Processor (ISP). Numerous low-level…
AIM 2022 Challenge on Super-Resolution of Compressed Image and Video: Dataset, Methods and Results
Ren Yang, Radu Timofte, Xin Li +49
This paper reviews the Challenge on Super-Resolution of Compressed Image and Video at AIM 2022. This challenge includes two tracks. Track 1 aims at the super-resolution of compress…
Conformer and Blind Noisy Students for Improved Image Quality Assessment
Marcos V. Conde, Maxime Burchi, Radu Timofte
Generative models for image restoration, enhancement, and generation have significantly improved the quality of the generated images. Surprisingly, these models produce more pleasa…
Model-Based Image Signal Processors via Learnable Dictionaries
Marcos V. Conde, Steven McDonagh, Matteo Maggioni +2
Digital cameras transform sensor RAW readings into RGB images by means of their Image Signal Processor (ISP). Computational photography tasks such as image denoising and colour con…