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NTIRE 2026 3D Restoration and Reconstruction in Real-world Adverse Conditions: RealX3D Challenge Results
Shuhong Liu, Chenyu Bao, Ziteng Cui +103
This paper presents a comprehensive review of the NTIRE 2026 3D Restoration and Reconstruction (3DRR) Challenge, detailing the proposed methods and results. The challenge seeks to…
Unrolled Decomposed Unpaired Learning for Controllable Low-Light Video Enhancement
Lingyu Zhu, Wenhan Yang, Baoliang Chen +4
Obtaining pairs of low/normal-light videos, with motions, is more challenging than still images, which raises technical issues and poses the technical route of unpaired learning as…
DDR: Exploiting Deep Degradation Response as Flexible Image Descriptor
Juncheng Wu, Zhangkai Ni, Hanli Wang +3
Image deep features extracted by pre-trained networks are known to contain rich and informative representations. In this paper, we present Deep Degradation Response (DDR), a method…
Opinion-Unaware Blind Image Quality Assessment using Multi-Scale Deep Feature Statistics
Zhangkai Ni, Yue Liu, Keyan Ding +3
Deep learning-based methods have significantly influenced the blind image quality assessment (BIQA) field, however, these methods often require training using large amounts of huma…
Misalignment-Robust Frequency Distribution Loss for Image Transformation
Zhangkai Ni, Juncheng Wu, Zian Wang +3
This paper aims to address a common challenge in deep learning-based image transformation methods, such as image enhancement and super-resolution, which heavily rely on precisely a…
ColNeRF: Collaboration for Generalizable Sparse Input Neural Radiance Field
Zhangkai Ni, Peiqi Yang, Wenhan Yang +3
Neural Radiance Fields (NeRF) have demonstrated impressive potential in synthesizing novel views from dense input, however, their effectiveness is challenged when dealing with spar…