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- NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results191
The NTIRE 2020 challenge addresses the real world setting, where paired true high and low-resolution images are unavailable, and the ultimate goal is to achieve the best perceptual quality, evaluated using a human study.
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- NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results85
This paper meticulously evaluates the state-of-the-art advancements in enhancing low-light images, reflecting the significant progress and creativity in this field.
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- AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results43
This paper introduces the real image Super-Resolution challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020, and gauges the state-of-the-art approaches for real image SR in terms of PSNR and SSIM.
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- NTIRE 2025 the 2nd Restore Any Image Model (RAIM) in the Wild Challenge24
This challenge established a new benchmark for real-world image restoration, featuring diverse scenarios with and without reference ground truth, and tasked participants with restoring real-captured images suffering from complex and unknown degradations.
- Ntire 2025 Image Shadow Removal Challenge Report22
This work examines the findings of the NTIRE 2025 Shadow Removal Challenge, with 17 teams successfully submitting their solutions during the final evaluation phase, and one focusing on reconstruction fidelity and the other on visual perception through a user study.
- NTIRE 2026 The 3rd Restore Any Image Model (RAIM) Challenge: AI Flash Portrait (Track 3)19
A comprehensive overview of the NTIRE 2026 3rd Restore Any Image Model (RAIM) challenge, with a specific focus on Track 3: AI Flash Portrait, and comprehensively evaluates the proposed algorithms utilizing a hybrid evaluation system that integrates objective quantitative metrics with rigorous subjective assessment protocols.
- Using less annotation workload to establish a pathological auxiliary diagnosis system for gastric cancer17
To help pathologists improve diagnostic accuracy and efficiency, 1,514 cases of stomach H&E-stained specimens with complete diagnostic information are collected to establish a pathological auxiliary diagnosis system based on deep learning.
- Structurally sophisticated 3D-printed PCL-fibrin hydrogel meniscal scaffold promotes in situ regeneration in the rabbit knee meniscus14
The integration of 3D printing technology and the Fib hydrogel provided a supportive microenvironment for chondrocyte proliferation and ECM secretion, facilitating the in situ regeneration and repair of the meniscal defect.
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- High-strength and high-elasticity silk fibroin-composite gelatin biomaterial hydrogels for rabbit knee cartilage regeneration10
It is established that high-strength SF/Gel hydrogel prepared by applying the binary-solvent-induced conformation transition strategy has potential applications in cartilage tissue repair and regeneration and is a feasible biomaterial for osteochondral regeneration.
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- A blind image super-resolution network guided by kernel estimation and structural prior knowledge8
This work proposes a two stage blind super-resolution network that is based on kernel estimation strategy and is capable of integrating structural texture as prior knowledge and outperforms the state-of-the-art methods in terms of fidelity and recovery of clear details.
- Integrated strategies in meniscus tissue engineering: from biomaterials to stem cell–driven regeneration7
Recent advances in scaffold design and cell-based therapies for meniscus repair are summarized and the convergence of materials science, stem cell biology, advanced fabrication, and computational modeling offers a promising roadmap toward clinically viable meniscus regeneration.
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- Auxiliary Diagnostic Method for Patellofemoral Pain Syndrome Based on One-Dimensional Convolutional Neural Network7
An intelligent diagnostic method for PFPS was proposed on the basis of a one-dimensional convolutional neural network (1D CNN), which used surface electromyography (sEMG) signals and lower limb joint angles as inputs and had the best performance on the running data set.
- Compact StereoNet: Stereo Disparity Estimation via Knowledge Distillation and Compact Feature Extractor7
An efficient convolutional neural architecture for stereo disparity estimation is proposed and a novel adaptive $Smooth_{L1}$ (ASL) Loss for calculating the similarity between the contextual features of the teacher network and those of the student network is presented, resulting in a more robust distillation process.
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- Toward Real World Stereo Image Super-Resolution via Hybrid Degradation Model and Discriminator for Implied Stereo Image Information5
A novel approach is proposed that integrates a implicit stereo information discriminator and a hybrid degradation model that ensures effective enhancement while preserving disparity consistency, and demonstrates impressive performance on synthetic and real datasets.
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- ScribbleVS: Scribble-Supervised Medical Image Segmentation via Dynamic Competitive Pseudo Label Selection4
This work introduces ScribbleVS, a framework designed to learn from scribble annotations, and introduces a Regional Pseudo Labels Diffusion Module to expand the scope of supervision and reduce the impact of noise present in pseudo labels.
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- E2-RealSR: efficient and effective real-world super-resolution network based on partial degradation modulation3
This work proposes an efficient and effective real-world image super-resolution network (E2-RealSR) based on partial degradation modulation, which is consisted of a small regression and a lightweight super-resolution network.
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- Research on the Application of Acoustic Simulation Based on iNoise in the Evaluation of Factory Boundary Noise3
Taking the noise control project of a natural gas purification plant as an example, a three-dimensional modeling and simulation analysis of the boundary noise of the plant by using the new acoustic simulation platform iNoise is made.
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- Ultrasound-optimized decellularization and functional modification for enhanced vascular grafts2
An innovative approach that integrates ultrasound optimization and functional modification, addressing the limitations of traditional decellularization methods is presented, offering a high-performance, low-toxicity strategy for developing vascular grafts with significant clinical potential, particularly for small-diameter vascular applications.
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- Research on Prediction Method of Objective Assessment of Building Acoustics Based on Machine Learning1
The neural network technology based on machine learning and computer acoustic simulation technology are combined to extract 10 typical characteristic parameters and 3 target parameters of 800 halls and rooms to predict the objective assessment of building acoustics more accurately and efficiently.
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- A Visual Recommendation Analysis System Based on the Interpretable Graph Neural Network–
This study converts the recommendation problem into the graph classification problem, and uses interpretable graph neural networks to explain the recommendation system, breaking through the previous situation in which explanations in recommendations were mostly at the instance level.
- ASteISR: Adapting Single Image Super-resolution Pre-trained Model for Efficient Stereo Image Super-resolution–
This paper introduces the concept of stereo adapters and spatial adapters which are incorporated into the pre-trained SISR transformer network and proposes a efficient method for transferring a pre-trained single-image super-resolution transformer network to the domain of stereo image super-resolution (SteISR) through a parameter-efficient fine-tuning (PEFT) method.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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