most citedReal-Time 4K Super-Resolution of Compressed AVIF Images. AIS 2024 Challenge Survey

2 citations · 2 across the 2 of their papers we have counts for

collaborators

6 papers

cs.CV20242 cited

Real-Time 4K Super-Resolution of Compressed AVIF Images. AIS 2024 Challenge Survey

Marcos V. Conde, Zhijun Lei, Wen Li +72

This paper introduces a novel benchmark as part of the AIS 2024 Real-Time Image Super-Resolution (RTSR) Challenge, which aims to upscale compressed images from 540p to 4K resolutio…

cs.CV2024

Deep RAW Image Super-Resolution. A NTIRE 2024 Challenge Survey

Marcos V. Conde, Florin-Alexandru Vasluianu, Radu Timofte +32

This paper reviews the NTIRE 2024 RAW Image Super-Resolution Challenge, highlighting the proposed solutions and results. New methods for RAW Super-Resolution could be essential in…

cs.CV2024

The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report

Bin Ren, Yawei Li, Nancy Mehta +129

This paper provides a comprehensive review of the NTIRE 2024 challenge, focusing on efficient single-image super-resolution (ESR) solutions and their outcomes. The task of this cha…

cs.CV2024

NTIRE 2024 Challenge on Image Super-Resolution (x4): Methods and Results

Zheng Chen, Zongwei Wu, Eduard Zamfir +85

This paper reviews the NTIRE 2024 challenge on image super-resolution (4), highlighting the solutions proposed and the outcomes obtained. The challenge involves generating…

cs.CV2024

HyCTAS: Multi-Objective Hybrid Convolution-Transformer Architecture Search for Real-Time Image Segmentation

Hongyuan Yu, Cheng Wan, Xiyang Dai +6

Real-time image segmentation demands architectures that preserve fine spatial detail while capturing global context under tight latency and memory budgets. Image segmentation is on…

eess.IV2023

Swift Parameter-free Attention Network for Efficient Super-Resolution

Cheng Wan, Hongyuan Yu, Zhiqi Li +5

Single Image Super-Resolution (SISR) is a crucial task in low-level computer vision, aiming to reconstruct high-resolution images from low-resolution counterparts. Conventional att…