Dictionary learning super resolution

WebJul 19, 2024 · We propose an end-to-end super-resolution network with a deep dictionary (SRDD), where a high-resolution dictionary is explicitly learned without sacrificing the advantages of deep learning. Web3D depth cameras have become more and more popular in recent years. However, depth maps captured by these cameras can hardly be used in 3D reconstruction directly because they often suffer from low resolution and blurring depth discontinuities. Super resolution of depth maps is necessary. In depth maps, the edge areas play more important role and …

Multi-Direction Dictionary Learning Based Depth Map Super-Resolution …

WebApr 8, 2024 · Multispectral (MS) sensors are mostly of low resolution (LR) and fail to give promising results in remote-sensing applications. In the recovery of edge information from LR images, the sparse representation-based single image super-resolution (SISR) employing patch-based dictionary alone does not give satisfactory results. To overcome … WebJan 28, 2024 · To improve the quality of the super-resolution (SR) reconstructed medical images, an improved adaptive multi-dictionary learning method is proposed, which uses the combined information of medical image itself and the natural images database. cymatics keys plugin https://numbermoja.com

Image Super-Resolution Using Compressed Sensing Based on Learning …

WebApr 3, 2012 · Abstract: In this paper, we propose a novel coupled dictionary training method for single-image super-resolution (SR) based on patchwise sparse recovery, where … WebI am currently working in the area of Image Processing and Computer Vision. My duties are to develop Machine Learning based algorithms to solve different ill-posed inverse problems in Digital Image Processing and Computer Vision Applications, e.g. Sparse representation based image super-resolution, Adaptive dictionary learning, Compressive sensing for … WebJun 1, 2024 · A novel multiclass dictionary learning method is proposed, in which depth image is divided into classified patches according to their geometrical directions and a sparse dictionary is trained within each class, which outperforms state-of-the-art methods in depth map super-resolution in terms of both subjective quality and objective quality. … cymatics keys instrument

MRI Super-Resolution with Ensemble Learning and …

Category:(PDF) Low-Dose Computed Tomography Image Super-Resolution ...

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Dictionary learning super resolution

Multitask dictionary learning and sparse representation …

WebA semi-coupled dictionary learning (SCDL) model is proposed in this paper to solve such cross-style image synthesis problems. Under SCDL, a pair of dictionaries and a mapping function will be simultaneously learned.

Dictionary learning super resolution

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WebConventional coupled dictionary learning approache. 展开 . 关键词： Image super-resolution Coupled dictionary leaming l(1)-norm Non-linear mapping Non-local self-similarity. WebNov 14, 2024 · This paper proposes a dictionary learning-based SR algorithm that extracts selective patches from an input LR image based on the iScore criterion. Results show …

WebMar 10, 2016 · Request PDF On Mar 10, 2016, Muhammad Sameer Sheikh published Image Super-Resolution Using Compressed Sensing Based on Learning Sub Dictionary Find, read and cite all the research you need on ... WebI completed my PhD at Nanyang Technological University (NTU) in Singapore. My research interests include: • Machine Learning & Deep Learning. • 2D & 3D Computer Vision. • Medical Image ...

WebSuper-resolution imaging (SR) is a class of techniques that enhance (increase) the resolution of an imaging system. In optical SR the diffraction limit of systems is transcended, while in geometrical SR the resolution of digital imaging sensors is enhanced.. In some radar and sonar imaging applications (e.g. magnetic resonance … WebJul 26, 2012 · A semi-coupled dictionary learning (SCDL) model is proposed in this paper to solve such cross-style image synthesis problems. Under SCDL, a pair of dictionaries and a mapping function will be simultaneously learned.

WebMay 1, 2024 · Special attention has been paid to the dictionary learning based methods, including single dictionary learning [21], dual-dictionary learning [19], multi-scale …

WebOct 1, 2011 · This work has investigated the use of dictionary learning to the super-resolution image reconstruction problem. In this paper, we firstly investigate the sparse … cymatics kitsWebJun 23, 2013 · The proposed semi-coupled dictionary learning (SCDL) model is applied to image super-resolution and photo-sketch synthesis, and the experimental results validated its generality and effectiveness in cross-style image synthesis. 564 Highly Influential PDF View 10 excerpts, references methods and background cymatics kickWebIn this paper, a new image enhance method is proposed to well boost the image saliency based on dictionary learning. In particular, the dictionary is learned from the sub- image blocks. The dictionary implies direct relevance to the image content. cymatics llcWebJun 1, 2024 · In recent years, the rapid development of deep learning in the field of multimedia processing, deep learning based super-resolution images restoration has … cymatics libreriasWebThe codes are to learn a group of dictionaries or sub-dictionaries which are then used to perform guided image super-resolution for a certain modality, e.g. near-infrared … cymatics labWebDue to the limitations of the resolution of the imaging system and the influence of scene changes and other factors, sometimes only low-resolution images can be acquired, … cymatics laptopWebOct 21, 2016 · In this paper, a new algorithm is proposed based on coupled dictionary learning with mapping function for the problem of single-image super-resolution. Dictionaries are designed for a set of clustered data. Data is classified into directional clusters by correlation criterion. cymatics lofi vst