activity
20182026
most citedLYT-NET: Lightweight YUV Transformer-based Network for Low-light Image Enhancement

5 citations · 7 across the 11 of their papers we have counts for

collaborators

12 papers

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

Multinex: Lightweight Low-light Image Enhancement via Multi-prior Retinex

Alexandru Brateanu, Tingting Mu, Codruta Ancuti +1

Low-light image enhancement (LLIE) aims to restore natural visibility, color fidelity, and structural detail under severe illumination degradation. State-of-the-art (SOTA) LLIE tec…

cs.CV2025

Real-World Scene Recovery for Scattering-Degraded Images Using Spatial and Frequency Priors

Yun Liu, Tao Li, Guanghui Yue +3

Scene recovery from real-world images degraded by scattering effects, such as haze, sandstorm, underwater, and remote sensing conditions, remains a fundamental yet challenging prob…

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

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement

Raul Balmez, Alexandru Brateanu, Ciprian Orhei +2

We introduce ISALux, a novel transformer-based approach for Low-Light Image Enhancement (LLIE) that seamlessly integrates illumination and semantic priors. Our architecture include…

cs.CV2025

ModalFormer: Multimodal Transformer for Low-Light Image Enhancement

Alexandru Brateanu, Raul Balmez, Ciprian Orhei +2

Low-light image enhancement (LLIE) is a fundamental yet challenging task due to the presence of noise, loss of detail, and poor contrast in images captured under insufficient light…

cs.CV2025

NTIRE 2025 Image Shadow Removal Challenge Report

Florin-Alexandru Vasluianu, Tim Seizinger, Zhuyun Zhou +79

This work examines the findings of the NTIRE 2025 Shadow Removal Challenge. A total of 306 participants have registered, with 17 teams successfully submitting their solutions durin…

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.CV2024★ 5 cited

LYT-NET: Lightweight YUV Transformer-based Network for Low-light Image Enhancement

A. Brateanu, R. Balmez, A. Avram +2

This letter introduces LYT-Net, a novel lightweight transformer-based model for low-light image enhancement (LLIE). LYT-Net consists of several layers and detachable blocks, includ…

cs.CV2020

NTIRE 2020 Challenge on NonHomogeneous Dehazing

Codruta O. Ancuti, Cosmin Ancuti, Florin-Alexandru Vasluianu +49

This paper reviews the NTIRE 2020 Challenge on NonHomogeneous Dehazing of images (restoration of rich details in hazy image). We focus on the proposed solutions and their results e…

cs.CV2019

Dense Haze: A benchmark for image dehazing with dense-haze and haze-free images

Codruta O. Ancuti, Cosmin Ancuti, Mateu Sbert +1

Single image dehazing is an ill-posed problem that has recently drawn important attention. Despite the significant increase in interest shown for dehazing over the past few years,…

cs.CV2018★ 2 cited

O-HAZE: a dehazing benchmark with real hazy and haze-free outdoor images

Codruta O. Ancuti, Cosmin Ancuti, Radu Timofte +1

Haze removal or dehazing is a challenging ill-posed problem that has drawn a significant attention in the last few years. Despite this growing interest, the scientific community is…