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20232026
most citedLicense Plate Super-Resolution Using Diffusion Models

12 citations · 16 across the 13 of their papers we have counts for

collaborators

13 papers

cs.CV2026

NTIRE 2026 Low-light Enhancement: Twilight Cowboy Challenge

Aleksei Khalin, Egor Ershov, Artyom Panshin +46

This paper presents a review of the NTIRE 2026 Low-light Enhancement: Twilight Cowboy Challenge. The objective of the competition was to merge a set of misaligned smartphone images…

cs.CV2026

Low Light Image Enhancement Challenge at NTIRE 2026

George Ciubotariu, Sharif S M A, Abdur Rehman +90

This paper presents a comprehensive review of the NTIRE 2026 Low Light Image Enhancement Challenge, highlighting the proposed solutions and final results. The objective of this cha…

cs.CV2026

NTIRE 2026 The Second Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results

Xin Li, Yeying Jin, Suhang Yao +95

This paper presents an overview of the NTIRE 2026 Second Challenge on Day and Night Raindrop Removal for Dual-Focused Images. Building upon the success of the first edition, this c…

cs.CV2026

Robust Deepfake Detection, NTIRE 2026 Challenge: Report

Benedikt Hopf, Radu Timofte, Chenfan Qu +54

Robustness is a long-overlooked problem in deepfake detection. However, detection performance is nearly worthless in the real world if it suffers under exposure to even slight imag…

cs.CV2026

The Fourth Challenge on Image Super-Resolution (4) at NTIRE 2026: Benchmark Results and Method Overview

Zheng Chen, Kai Liu, Jingkai Wang +150

This paper presents the NTIRE 2026 image super-resolution (4) challenge, one of the associated competitions of the NTIRE 2026 Workshop at CVPR 2026. The challenge aims to r…

cs.CV2026

NTIRE 2026 Challenge on Robust AI-Generated Image Detection in the Wild

Aleksandr Gushchin, Khaled Abud, Ekaterina Shumitskaya +51

This paper presents an overview of the NTIRE 2026 Challenge on Robust AI-Generated Image Detection in the Wild, held in conjunction with the NTIRE workshop at CVPR 2026. The goal o…

cs.CV2026

NTIRE 2026 Challenge on Single Image Reflection Removal in the Wild: Datasets, Results, and Methods

Jie Cai, Kangning Yang, Zhiyuan Li +50

In this paper, we review the NTIRE 2026 challenge on single-image reflection removal (SIRR) in the wild. SIRR is a fundamental task in image restoration. Despite progress in academ…

cs.CV2025

NTIRE 2025 Challenge on Low Light Image Enhancement: Methods and Results

Xiaoning Liu, Zongwei Wu, Florin-Alexandru Vasluianu +102

This paper presents a comprehensive review of the NTIRE 2025 Low-Light Image Enhancement (LLIE) Challenge, highlighting the proposed solutions and final outcomes. The objective of…

cs.CV2025

Dual-Stage Global and Local Feature Framework for Image Dehazing

Anas M. Ali, Anis Koubaa, Bilel Benjdira

Addressing the challenge of removing atmospheric fog or haze from digital images, known as image dehazing, has recently gained significant traction in the computer vision community…

cs.CV2025

The Tenth NTIRE 2025 Image Denoising Challenge Report

Lei Sun, Hang Guo, Bin Ren +91

This paper presents an overview of the NTIRE 2025 Image Denoising Challenge (σ = 50), highlighting the proposed methodologies and corresponding results. The primary objective is to…

cs.CV2025★ 2 cited

NTIRE 2025 Challenge on Image Super-Resolution (x4): Methods and Results

Zheng Chen, Kai Liu, Jue Gong +108

This paper presents the NTIRE 2025 image super-resolution (4) challenge, one of the associated competitions of the 10th NTIRE Workshop at CVPR 2025. The challenge aims to r…

cs.CV2023★ 2 cited

Guided Frequency Loss for Image Restoration

Bilel Benjdira, Anas M. Ali, Anis Koubaa

Image Restoration has seen remarkable progress in recent years. Many generative models have been adapted to tackle the known restoration cases of images. However, the interest in b…

cs.CV2023★ 12 cited

License Plate Super-Resolution Using Diffusion Models

Sawsan AlHalawani, Bilel Benjdira, Adel Ammar +2

In surveillance, accurately recognizing license plates is hindered by their often low quality and small dimensions, compromising recognition precision. Despite advancements in AI-b…