Dictionary learning super resolution

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August undefined, 2024

WebApr 8, 2024 · Dictionary learning is an essential step in sparse coding-based approaches for obtaining single or coupled overcomplete dictionaries by training over LR and HR image patches collected from a global or single image database. 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 …

Super-resolution imaging - Wikipedia

Websuper-resolution; sparse autoencoder; dictionary learning; ZCA whitening 1. Introduction In the remote sensing, medical, military, and other fields, the acquisition of high-resolution (HR) images is of great significance. WebIn 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. image tumblers

[2207.09228] Image Super-Resolution with Deep Dictionary - arXiv

WebSep 30, 2015 · We build an example prototype using a Lytro camera (380×380 pixel spatial resolution) and a 18 megapixel (MP) Canon DSLR camera to generate a light field with … WebOct 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. WebAiming at reducing computed tomography (CT) scan radiation while ensuring CT image quality, a new low-dose CT super-resolution reconstruction method based on combining a random forest with coupled dictionary learning is proposed. The random forest image turtle

Improved dictionary learning algorithm with mappings for single …

Dictionary learning-based image super-resolution for …

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 … WebMay 1, 2024 · Abstract and Figures Theoretically, the Super-Resolution (SR) reconstruction scheme is a method which is performed by many applications nowadays for the purpose of generating a... image turfWebAug 17, 2024 · A sparse representation (using self-example dictionary learning)-based framework for denoising and super-resolution (SR) is proposed. The proposed scheme makes use of fast nonnegative orthogonal matching pursuit for the sparse coding. The dictionary learning is implemented using the K-singular value decomposition. list of direct lending funds

"Webdecompression transformer super-resolution image-denoising image-restoration restoration denoising image-super-resolution low-level-vision deblocking vision … " - Dictionary learning super resolution

Dictionary learning super resolution

[2207.09228] Image Super-Resolution with Deep Dictionary - arXiv

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.

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WebJan 1, 2024 · Abstract. Inspired by the recent success of deep neural networks and the recent efforts to develop multi-layer dictionary models, we propose a Deep Analysis … 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 …

WebIn this paper a coupled dictionary and mapping function learning algorithm is proposed for the task of single image super-resolution. The proposed algorithm consists of dictionary learning part and image reconstruction part. In the dictionary training stage we propose a coupled dictionary and mapping function learning using the K-singular value … Webimage resolution [5], [6], and are also considered as model-based algorithms. Dictionary learning-based super-resolution techniques [7] -[9] are examples of learning based algorithms. Recently, with the rapid evolution of machine learning, especially deep learning, deep neural networks have become popular among SR studies.

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 … WebThe 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 …

WebAiming at reducing computed tomography (CT) scan radiation while ensuring CT image quality, a new low-dose CT super-resolution reconstruction method based on …

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 … image tv station news vanWeb3D 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 … image tv show repairsWebAiming at reducing computed tomography (CT) scan radiation while ensuring CT image quality, a new low-dose CT super-resolution reconstruction method based on … list of direct flights from aberdeen airportWebJan 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. image turnipWebJun 11, 2024 · Therefore, the utilization of super resolution (SR) based on dictionary learning and sparse representation is necessary to obtain high-resolution (HR) images … image tv reduiteWebNov 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 … list of direct listingsWebJul 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 … image twd