Supavadee Aramvith
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View article: MCA: 2D-3D Retrieval with Noisy Labels via Multi-level Adaptive Correction and Alignment
MCA: 2D-3D Retrieval with Noisy Labels via Multi-level Adaptive Correction and Alignment Open
With the increasing availability of 2D and 3D data, significant advancements have been made in the field of cross-modal retrieval. Nevertheless, the existence of imperfect annotations presents considerable challenges, demanding robust solu…
View article: XTNSR: Xception-based transformer network for single image super resolution
XTNSR: Xception-based transformer network for single image super resolution Open
Single image super resolution has significantly advanced by utilizing transformers-based deep learning algorithms. However, challenges still need to be addressed in handling grid-like image patches with higher computational demands and add…
View article: HybridATNet: Multi-Scale Attention and Hybrid Feature Refinement Network for Remote Sensing Image Super-Resolution
HybridATNet: Multi-Scale Attention and Hybrid Feature Refinement Network for Remote Sensing Image Super-Resolution Open
Remote sensing image super-resolution faces significant challenges, including detail loss, sensor noise, and atmospheric interference that compromise image quality for critical applications. Current deep learning approaches struggle with t…
View article: Superpixel-Guided Graph-Attention Boundary GAN for Adaptive Feature Refinement in Scribble-Supervised Medical Image Segmentation
Superpixel-Guided Graph-Attention Boundary GAN for Adaptive Feature Refinement in Scribble-Supervised Medical Image Segmentation Open
Fully supervised medical image segmentation still relies on labor-intensive, pixel-level annotations, which limits scale across cohorts and imaging settings. Scribble supervision reduces this burden, yet many CNN-based methods struggle und…
View article: AERU-Net: Adaptive Edge Recovery and Attention U-Shaped Network for Remote Sensing Image Super-Resolution
AERU-Net: Adaptive Edge Recovery and Attention U-Shaped Network for Remote Sensing Image Super-Resolution Open
Remote sensing image super-resolution (RSISR) has recently garnered significant attention, with major advances achieved through deep learning neural networks. Remote sensing images (RSIs) contain rich, recurring textural and structural pat…
View article: MADN: Multi-Attention With Diffusion Network for Scene Text Image Super-Resolution
MADN: Multi-Attention With Diffusion Network for Scene Text Image Super-Resolution Open
Scene Text Image Super-Resolution (STISR) enhances the recognition accuracy of degraded textual imagery that requires fine-grained character reconstruction. Despite incorporating text-specific prior knowledge, existing methods suffer from …
View article: An Advanced Features Extraction Module for Remote Sensing Image Super-Resolution
An Advanced Features Extraction Module for Remote Sensing Image Super-Resolution Open
In recent years, convolutional neural networks (CNNs) have achieved remarkable advancement in the field of remote sensing image super-resolution due to the complexity and variability of textures and structures in remote sensing images (RSI…
View article: SEGSRNet for Stereo-Endoscopic Image Super-Resolution and Surgical Instrument Segmentation
SEGSRNet for Stereo-Endoscopic Image Super-Resolution and Surgical Instrument Segmentation Open
SEGSRNet addresses the challenge of precisely identifying surgical instruments in low-resolution stereo endoscopic images, a common issue in medical imaging and robotic surgery. Our innovative framework enhances image clarity and segmentat…
View article: ECTI DAMT & NCON 2024 Content Announcement Page
ECTI DAMT & NCON 2024 Content Announcement Page Open
It is with great pleasure and excitement that I welcome you to the 9th International Conference on Digital Arts, Media and Technology (DAMT) and the 7th ECTI Northern Section Conference on Electrical, Electronics, Computer, and Telecommuni…
View article: ECTI DAMT & NCON 2024 Content Announcement Page
ECTI DAMT & NCON 2024 Content Announcement Page Open
AI Based Video Analytics:In this talk, we will present and discuss the current trends and researches in video analytics.As surveillance cameras have been widely installed worldwide, although the main purpose of those cameras is for monitor…
View article: MSRFSR: Multi-Stage Refining Face Super-Resolution With Iterative Collaboration Between Face Recovery and Landmark Estimation
MSRFSR: Multi-Stage Refining Face Super-Resolution With Iterative Collaboration Between Face Recovery and Landmark Estimation Open
Face Super-resolution (FSR) models encounter a significant challenge related to extremely low-dimensional ( pixels) and degraded input images. This deficiency in crucial facial details within the low-level and intermediate levels of the FS…
View article: Saliency-Aware Deep Learning Approach for Enhanced Endoscopic Image Super-Resolution
Saliency-Aware Deep Learning Approach for Enhanced Endoscopic Image Super-Resolution Open
The adoption of Stereo Imaging technology within endoscopic procedures represents a transformative advancement in medical imaging, providing surgeons with depth perception and detailed views of internal anatomy for enhanced diagnostic accu…
View article: Corrections to “MSRFSR: Multi-Stage Refining Face Super-Resolution With Iterative Collaboration Between Face Recovery and Landmark Estimation”
Corrections to “MSRFSR: Multi-Stage Refining Face Super-Resolution With Iterative Collaboration Between Face Recovery and Landmark Estimation” Open
Presents corrections to the paper, (Corrections to “MSRFSR: Multi-Stage Refining Face Super-Resolution With Iterative Collaboration Between Face Recovery and Landmark Estimation”).
View article: CANS: Combined Attention Network for Single Image Super-Resolution
CANS: Combined Attention Network for Single Image Super-Resolution Open
Single image super-resolution (SISR) is a rapidly advancing area that has attracted considerable interest in recent years, largely due to the successful use of deep convolutional neural networks (CNNs). This growth can be attributed to sev…
View article: E-SEVSR—Edge Guided Stereo Endoscopic Video Super-Resolution
E-SEVSR—Edge Guided Stereo Endoscopic Video Super-Resolution Open
Integrating Stereo Imaging technology into medical diagnostics and surgeries marks a significant revolution in medical sciences. This advancement gives surgeons and physicians a deeper understanding of patients’ organ anatomy. However, lik…
View article: Light the Way: An Enhanced Generative Adversarial Network Framework for Night-to-Day Image Translation With Improved Quality
Light the Way: An Enhanced Generative Adversarial Network Framework for Night-to-Day Image Translation With Improved Quality Open
Driving at night introduces considerable challenges due to reduced visibility, making it essential to explore techniques that enhance road information for drivers. With this purview, the research presents a technique to address visibility …
View article: Brain MRI Image Super-Resolution Reconstruction: A Systematic Review
Brain MRI Image Super-Resolution Reconstruction: A Systematic Review Open
Magnetic Resonance Imaging (MRI) is pivotal in clinical diagnostics and neurological research, providing high-contrast, non-invasive imaging. However, the acquisition of high-resolution MRI is hampered by hardware limitations, extended sca…
View article: DHTCUN: Deep Hybrid Transformer CNN U Network for Single-Image Super-Resolution
DHTCUN: Deep Hybrid Transformer CNN U Network for Single-Image Super-Resolution Open
Recent advances in image super-resolution have investigated various transformer and CNN techniques to improve quantitative and perceptual outcomes. Reconstructing high-resolution images from their low-resolution equivalents by combining th…
View article: Transformer’s Role in Brain MRI: A Scoping Review
Transformer’s Role in Brain MRI: A Scoping Review Open
Magnetic Resonance Imaging (MRI) is a critical imaging technique that provides detailed visualization of internal structures without harmful radiation. This review focuses on key MRI modalities, including T1-weighted and T2-weighted imagin…
View article: ISCIT 2023 Committees
ISCIT 2023 Committees Open
View article: Learning-Based Rate Control for High Efficiency Video Coding
Learning-Based Rate Control for High Efficiency Video Coding Open
High efficiency video coding (HEVC) has dramatically enhanced coding efficiency compared to the previous video coding standard, H.264/AVC. However, the existing rate control updates its parameters according to a fixed initialization, which…
View article: Fusion Objective Function on Progressive Super-Resolution Network
Fusion Objective Function on Progressive Super-Resolution Network Open
Recent advancements in Single-Image Super-Resolution (SISR) have explored the network architecture of deep-learning models to achieve a better perceptual quality of super-resolved images. However, the effect of the objective function, whic…
View article: DANS: Deep Attention Network for Single Image Super-Resolution
DANS: Deep Attention Network for Single Image Super-Resolution Open
The current advancements in image super-resolution have explored different attention mechanisms to achieve better quantitative and perceptual results. The critical challenge recently is to utilize the potential of attention mechanisms to r…
View article: Multi-FusNet of Cross Channel Network for Image Super-Resolution
Multi-FusNet of Cross Channel Network for Image Super-Resolution Open
Image Super-resolution (SR) has gained considerable attention in artificial intelligence (AI) research and image-based applications. Recent deep learning-based SR models have demonstrated remarkable accuracy and perceptual quality in the r…
View article: SENext: Squeeze-and-ExcitationNext for Single Image Super-Resolution
SENext: Squeeze-and-ExcitationNext for Single Image Super-Resolution Open
Recent research on image and video processing using convolutional neural networks has shown remarkable improvements, especially in the area of single image super-resolution(SISR). The primary target of SISR is to recover the visually appea…
View article: Reducing Complexity on Coding Unit Partitioning in Video Coding: A Review
Reducing Complexity on Coding Unit Partitioning in Video Coding: A Review Open
In this article, we present a survey on the low complexity video coding on a coding unit (CU) partitioning with the aim for researchers to understand the foundation of video coding and fast CU partition algorithms.Firstly, we introduce vid…
View article: Senext: Squeeze-and-Excitationnext for Single Image Super-Resolution
Senext: Squeeze-and-Excitationnext for Single Image Super-Resolution Open
View article: Video anomaly detection using deep residual-spatiotemporal translation network
Video anomaly detection using deep residual-spatiotemporal translation network Open
View article: Low complexity mode selection for H.266/VVC intra coding
Low complexity mode selection for H.266/VVC intra coding Open
The newest video compression standard, called Versatile Video Coding (H.266/VVC), outperforms its predecessor High Efficiency Video Coding (HEVC) by up to 50% in the matter of coding performance. The achievement results from the integratin…
View article: Multi-scale Xception based depthwise separable convolution for single image super-resolution
Multi-scale Xception based depthwise separable convolution for single image super-resolution Open
The main target of Single image super-resolution is to recover high-quality or high-resolution image from degraded version of low-quality or low-resolution image. Recently, deep learning-based approaches have achieved significant performan…