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Works54 from public data
- RescueNet: An unpaired GAN for brain tumor segmentation182
A network architecture named as residual cyclic unpaired encoder-decoder network (RescueNet) is designed using residual and mirroring principles to segment the whole tumor followed by core and enhance regions in a brain MRI scan.
- Burst Image Restoration and Enhancement129
The central idea is to create a set of pseudo-burst features that combine complimentary information from all the input burst frames to seamlessly exchange information, and the approach initially extracts pre-processed features from each burst frame and matches them using an edge-boosting burst alignment module.
- EarthDial: Turning Multi-sensory Earth Observations to Interactive Dialogues110
EarthDial is introduced, a conversational assistant specifically designed for Earth Observation (EO) data, transforming complex, multi-sensory Earth observations into interactive, natural language dialogues, and outperforms existing generic and domain-specific models, achieving better generalization across various EO tasks.
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- Burstormer: Burst Image Restoration and Enhancement Transformer79
This work proposes Burstormer: a novel transformer-based architecture for burst image restoration and enhancement that outperforms state-of-the-art methods on burst super-resolution, burst denoising and burst low-light enhance-ment.
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- C^2MSNet: A Novel Approach for Single Image Haze Removal60
Performance analysis shows that the proposed cardinal (red, green and blue) color fusion network for single image haze removal outperforms the existing state-of-the-art methods forsingle image dehazing.
- ThinkGeo: Evaluating Tool-Augmented Agents for Remote Sensing Tasks49
ThinkGeo, an agentic benchmark designed to evaluate LLM-driven agents on remote sensing tasks via structured tool use and multi-step planning, provides the first extensive testbed for evaluating how tool-enabled LLMs handle spatial reasoning in remote sensing.
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- Multi‐frame based adversarial learning approach for video surveillance45
A novel multi-frame-based adversarial learning network is proposed with multi-scale inception and residual module for FBS to learn prominent features of the foreground object(s) to exhibit more temporal consistency.
- An Unified Recurrent Video Object Segmentation Framework for Various Surveillance Environments45
It is demonstrated that the proposed method outperforms the state-of-the-art methods for MOS without any pre-trained module, fine-tuning on the test video frame(s) or complicated training.
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- The Tenth NTIRE 2025 Efficient Super-Resolution Challenge Report39
This paper presents a comprehensive review of the NTIRE 2025 Challenge on Single-Image Efficient Super-Resolution, emphasizing groundbreaking advancements in state-of-the-art single-image ESR techniques.
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- AVisT: A Benchmark for Visual Object Tracking in Adverse Visibility36
AVisT is introduced, a dedicated benchmark for visual tracking in diverse scenarios with adverse visibility and can greatly benefit the tracking community by complementing the existing benchmarks, in developing new creative tracking solutions in order to continue pushing the boundaries of the state-of-the-art.
- The Tenth NTIRE 2025 Image Denoising Challenge Report34
An overview of the NTIRE 2025 Image Denoising Challenge is presented, highlighting the proposed methodologies and corresponding results, providing insights into the current state-of-the-art in image denoising.
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- NTIRE 2026 Challenge on Robust AI-Generated Image Detection in the Wild29
This report provides a comprehensive overview of the NTIRE 2026 Challenge on Robust AI-Generated Image Detection in the Wild, describes the proposed solutions, and can be used as a valuable reference for researchers and practitioners in increasing the robustness of the detection models to real-world transformations.
- Robust Deepfake Detection, NTIRE 2026 Challenge: Report26
This report presents the competition setting, dataset preparation, as well as details and performance of methods, which rely on large foundation models, ensembles, and degradation training to combine generality and robustness in deepfake detection.
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- Interstitial Lung Disease Classification Using Feed Forward Neural Networks26
Experimental results with proposed scheme outperforms in classification of ILD patterns as well as different classifiers such as k-NN and SVM.
- The Fourth Challenge on Image Super-Resolution ($\times$4) at NTIRE 2026: Benchmark Results and Method Overview24
The NTIRE 2026 image super-resolution ($\times$4) challenge, one of the associated competitions of the NTIRE 2026 Workshop at CVPR 2026, provides a unified benchmark and offers insights into current progress and future directions in image super-resolution.
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- Low Light Image Enhancement Challenge at NTIRE 202623
This paper thoroughly evaluates the state-of-the-art advances in (joint denoising and) low-light image enhancement, showcasing the significant progress in the field, while leveraging samples of the authors' novel dataset.
- NTIRE 2025 Challenge on Image Super-Resolution ($\times 4$): Methods and Results23
The NTIRE 2025 image super-resolution ($\times 4$) challenge, one of the associated competitions of the 10th NTIRE Workshop at CVPR 2025, aims to recover high-resolution images from low-resolution counterparts generated through bicubic downsampling with a $\times 4$ scaling factor.
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- Gated Multi-Resolution Transfer Network for Burst Restoration and Enhancement21
A novel Gated Multi-Resolution Transfer Network (GMTNet) is proposed to reconstruct a spatially precise high-quality image from a burst of low-quality raw images and sets a state-of-the-art for burst super-resolution, burst denoising, and low-light burst enhancement.
- Dynamic Pre-training: Towards Efficient and Scalable All-in-One Image Restoration19
This work proposes DyNet, a dynamic family of networks designed in an encoder-decoder style for all-in-one image restoration tasks, and introduces a dynamic pre-training strategy that trains variants of the proposed DyNet concurrently, thereby achieving a 50% reduction in GPU hours.
- Local Binary Patterns Descriptor Based on Sparse Curvelet Coefficients for False-Positive Reduction in Mammograms17
New FP reduction technique has been proposed for breast cancer diagnosis based on appropriate integration of preprocessing, Self-organizing map (SOM) clustering, region of interest (ROI) extraction, and FP reduction based on proposed local sparse curvelet coefficients.
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- Multi-Scale Directional Mask Pattern for Medical Image Classification and Retrieval14
Performance analysis shows that the proposed feature descriptor outperforms the other existing state-of-the-art methods in terms of average recognition rate (ARR) and F-score.
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- Motion estimation in hazy videos13
A cascaded architecture i.e. haze removal followed by optical flow for motion estimation in hazy videos is proposed and the proposed image de-hazing network is build upon the Residual and Inception module concepts and named as ResINet.
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- Clear Roads, Clear Vision: Advancements in Multi-Weather Restoration for Smart Transportation3
This survey presents a comprehensive review of image and video restoration techniques developed to mitigate weather-induced visual impairments, categorizing existing approaches into traditional prior-based methods and modern data-driven models, including CNNs, transformers, diffusion models, and emerging vision-language models.
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- A Novel Saliency-Based Cascaded Approach for Moving Object Segmentation2
The performance of proposed method is significantly improved as compared to existing methods in terms of accuracy and computational complexity for MOS task.
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- LEDNet–
Experiments on benchmark datasets show that LEDNet outperforms the existing state-of-the-art methods for single image haze removal and local air-light estimation (LAE) algorithm, which is able to obtain the air- light component of the outdoor scene.
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 2026-10-14. 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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