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Works30 from public data
- 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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- CoSeR: Bridging Image and Language for Cognitive Super-Resolution93
The Cognitive Super-Resolution (CoSeR) framework is introduced, empowering SR models with the capacity to comprehend low-resolution images and a novel condition injection scheme called “Ali-in-Attention ”, consolidating all conditional information into a single module.
- Verification and control of partially observable probabilistic systems87
Probabilistic temporal logics are given that can express a range of quantitative properties of partially observable, probabilistic systems for both discrete and dense models of time, relating to the probability of an event’s occurrence or the expected value of a reward measure.
- Locally Adaptive Structure and Texture Similarity for Image Quality Assessment76
A locally adaptive structure and texture similarity index for full-reference IQA, which is term A-DISTS, which relies on a single statistical feature, namely the dispersion index, to localize texture regions at different scales.
- AIM 2019 Challenge on Constrained Super-Resolution: Methods and Results63
This paper reviews the AIM 2019 challenge on constrained example-based single image super-resolution with focus on proposed solutions and results and gauges the state-of-the-art in single imagesuper-resolution.
- Unsupervised Image Super-Resolution with an Indirect Supervised Path62
A novel framework which is composed of two stages: unsupervised image translation between real LR and synthetic LR images; and supervised super-resolution from approximated real LR images to the paired HR images, which achieves very good performance on datasets of NTIRE 2017, NTIRE 2018 and NTIRE 2020.
- NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results43
This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results, and gauge the state of the art of video quality enhancement.
- NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results43
The NTIRE2021 challenge on burst super-resolution was to generate a clean RGB image with 4 times higher resolution given a RAW noisy burst as input, and the top-performing methods set a new state-of-the-art for the burstsuper-resolution task.
- 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.
- NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results41
This paper reviews the NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video and proposed the LDV 2.0 dataset, which includes theLDV dataset (240 videos) and 95 additional videos.
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- Low-Res Leads the Way: Improving Generalization for Super-Resolution by Self-Supervised Learning37
This work introduces a novel “Low-Res Leads the Way” (LWay) training framework, merging Supervised Pre-training with Self-supervised Learning to enhance the adaptability of SR models to real-world images.
- NTIRE 2021 Challenge on Video Super-Resolution36
This paper presents evaluation results from two competition tracks as well as the proposed solutions to the NTIRE 2021 Challenge on Video Super-Resolution, and develops conventional video SR methods focusing on the restoration quality.
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- Unsupervised Flow-Aligned Sequence-to-Sequence Learning for Video Restoration30
This work proposes an unsupervised flow-aligned sequence-to-sequence model (S2SVR), which shows superior performance in multiple VR tasks, including video deblurring, video super-resolution, and compressed video quality enhancement.
- AsConvSR: Fast and Lightweight Super-Resolution Network with Assembled Convolutions29
This work analyzes the applications of the idea of divide-and-conquer in super-resolution, and proposes assembled convolutions which can adapt convolution kernels according to the input features, and outperforms all the state-of-the-art efficient super- resolution models.
- Safety Validation of Sense and Avoid Algorithms Using Simulation and Evolutionary Search26
A safety validation approach for Sense and Avoid (SAA) algorithms aboard Unmanned Aerial Vehicles (UAVs) by accelerating the process of finding dangerous faults of SAA algorithms and supporting the safety validation process.
- Adaptive Local Implicit Image Function for Arbitrary-Scale Super-Resolution23
This paper proposes a novel adaptive local image function (A-LIIF), which consists of two main components: an encoder and a expansion network that models the continuous up-scaling function by a weighted combination of multiple local implicit image functions.
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- Testing Method for Multi-UAV Conflict Resolution Using Agent-Based Simulation and Multi-Objective Search19
A new approach to testing multi-UAV conflict resolution algorithms is presented, which describes pairwise encounters in three-dimensional space using a parameterized geometry representation, which allows encounters involving multiple UAVs to be generated by combining several Pairwise encounters.
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- Verification and Control of Partially Observable Probabilistic Real-Time Systems14
The approach is based on an integer discretisation of the model’s dense-time behaviour and a grid-based abstraction of the uncountable belief space induced by partial observability, and it is shown how both lower and upper bounds on numerical results can be generated.
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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 2026-10-12. 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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