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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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- SGINet: Toward Sufficient Interaction Between Single Image Deraining and Semantic Segmentation35
A Semantic Guided Interactive Network (SGINet), which considers the sufficient interaction between SID and semantic segmentation using a three-stage deraining manner, i.e., coarse deraining, semantic information extraction, and semantics guided deraining.
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- FPGA Implementation of Digital Filters Synthesized Using the FRM Technique25
Some useful techniques for reducing the data transfer between the field programmable gate array (FPGA) and external memory when the random logic is implemented using the FPGA and the delay elements are implemented using an external memory such as dynamic random access memory.
- Improved Object Detection Algorithm of YOLOv3 Remote Sensing Image22
The feature extraction network DarkNet53 is strengthened to improve the ability of feature extraction and the original Leaky ReLU activation function is replaced by the Mish activation function, thus improving the generalization of the method in this paper.
- MIPI 2023 Challenge on Nighttime Flare Removal: Methods and Results18
The developed solutions in this challenge achieved state-of-the-art performance on Nighttime Flare Removal and a detailed description of all models developed is provided in this paper.
- Deep Multi-Resolution Mutual Learning for Image Inpainting17
This paper designs a new image inpainting network, termed multi-resolution mutual network (MRM-Net), which takes the damaged images of different resolutions as input, then excavates and exploits the correlation among different resolutions to guide the image inPainting process.
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- GCM-Net: Towards Effective Global Context Modeling for Image Inpainting16
A new image inpainting method termed Global Context Modeling Network (GCM-Net), which can potentially improve the performance of recovering the missing region in the damaged images with irregular masks by capturing the global contextual information.
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- FRC-Net: A Simple Yet Effective Architecture for Low-Light Image Enhancement13
This paper presents a novel full-resolution representation strategy to replace all feature scaling operations, which can prevent the information degradation by making the intermediary features keep the original resolution.
- Brillouin Frequency Shift Extraction Based on AdaBoost Algorithm13
Results show that the performance of BFS extraction decreases with the decrease in SNR, the reduction in frequency range, and the increase in frequency step.
- Learning Deep Non-blind Image Deconvolution Without Ground Truths13
An unsupervised deep learning approach for NBID which avoids accessing GT images is proposed and provides very competitive performance to existing supervised learning-based methods, no matter under accurate kernels or erroneous kernels.
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- Group-Based Sparse Representation for Compressed Sensing Image Reconstruction with Joint Regularization10
A joint regularized image reconstruction model based on group sparse representation (GSR-JR) that ensures the sparsity of the group coefficients and reduces the complexity of the model is proposed.
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- Improve follicular thyroid carcinoma diagnosis using computer aided diagnosis system on ultrasound images9
The results show the proposed CAD approach can not only achieve better performance than radiologists, but also significantly improve the radiologists’ diagnosis of FTC.
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- Hybrid High-Order and Fractional-Order Total Variation with Nonlocal Regularization for Compressive Sensing Image Reconstruction5
A hybrid high-order and fractional-order total variation with nonlocal regularization algorithm that does not only yield higher peak-signal-to-noise ratio (PSNR) and structural similarity (SSIM) but also retain abundant details and textures efficiently.
- Adaptive Weighted High Frequency Iterative Algorithm for Fractional-Order Total Variation with Nonlocal Regularization for Image Reconstruction5
The improved approach preserves more texture details and has better visual effects, especially at low sampling ratios, at the cost of taking more time.
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- Extraction of Brillouin frequency shift in Brillouin distributed fiber sensors by neighbors-based machine learning4
The results show that the method of neighbor-based machine learning is more robust under a wide range of signal-to-noise ratios, pump pulse widths, and frequency scanning steps.
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- Block compressed sensing image reconstruction via deep learning with smoothed projected Landweber3
A block-based CS (BCS) image reconstruction framework via a deep learning network with smoothed projected Landweber (SPL) method, which is not only far superior to other state-of-the-art CS algorithms in terms of reconstruction time and quality but also comparable with up-to-date deep learning methods.
- Multimodal Large Language Models for Multi-Subject In-Context Image Generation2
This work proposes MUSIC, the first MLLM specifically designed for multi-subject in-context generation and introduces an automatic and scalable data generation pipeline that eliminates the need for manual annotation, and curates MSIC, a new benchmark tailored for multi-subject in-context generation.
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- Multiscale deep network for compressive sensing image reconstruction2
Experimental results indicate that the proposed network framework outperforms the existing CS methods in terms of objective metrics, peak signal to noise ratio (PSNR), structural similarity index, and subjective visual quality.
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- Bitcoin Price Prediction Using Enhanced Transformer and Greed Index1
An adaptive decomposition method based on transformer is proposed, which combines transformer with seasonal trends and external factors, and shows that the method can significantly improve the performance of the baseline model.
- Nonlocal Low-Rank Regularization Combined with Bilateral Total Variation for Compressive Sensing Image Reconstruction1
A compressive sensing image reconstruction algorithm that combines bilateral total variation and nonlocal low-rank regularization to overcome over-smoothing and degradation of edge information which result from the prior reconstructed image is proposed.
- An Introduction of Design Approaches Defined in EN 1993 - 1 - 61
It is illustrated that EN 1993-1-6 has advantages over GB 150, and is equal to ASME Ⅷ-2,EN 13445, and the results demonstrateEN 1993- 1-6 is more economic thanGB 150, its design approaches are advanced.
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- CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution–
This work proposes a simple yet effective baseline called CubeFormer, designed to enhance feature richness by completing holistic information aggregation, and introduces cube attention, which expands 2D attention to 3D space, facilitating exhaustive information interactions, further encouraging comprehensive information extraction and promoting feature variety.
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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 2026-10-14. 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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