Super Resolution for Multimedia, Image, and Video Processing Applications

Malczewski, Krzysztof; Stasinski, Ryszard

doi:10.1007/978-3-642-02900-4_8

Cited by 17 publications

(6 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The SISR is an ill‐posed inverse problem without a unique solution in that numerous HR images can be converted into the same LR image. The SISR has been used on various range of image processing tasks contributing to commercial, government and scientific applications (Malczewski & Stasiński, 2009). Climate research communities have also applied SISR to improve downscaling techniques (Cheng et al., 2021; Rodrigues et al., 2018; Sekiyama, 2020).…”

Section: Methodsmentioning

confidence: 99%

Deep Learning Provides Substantial Improvements to County‐Level Fire Weather Forecasting Over the Western United States

Son.

Wang

et al. 2022

J Adv Model Earth Syst

View full text Add to dashboard Cite

Forecasting wildfire danger is a challenging task due to its complexity involving climate system, interactions with vegetation and socio-economic components (Hantson et al., 2016). As result of the remarkable progress in numerical weather forecasting (Bauer et al., 2015), its application for fire forecasting has also advanced, and some government agencies are now providing fire weather forecasting services. For instance, the National Interagency Coordination Center (NICC, https://www.nifc.gov/nicc/predictive/outlooks/outlooks.htm) issues public service forecasts of fire hazard in the United States for up to 7 days (called the 7-Day Significant Fire Potential Outlook). These forecasts categorize the fire potential into nine levels, considering fuel dryness, fire weather, ignition triggers, and resource capability. The European Forest Fire Information System (EFFIS, https://effis.jrc. ec.europa.eu) or the Global Wildfire Information System (GWIS, https://gwis.jrc.ec.europa.eu/) also provide fire danger forecast using four different indices based on the Fire Weather Index (FWI) system.

show abstract

Section: Methodsmentioning

confidence: 99%

Deep Learning Provides Substantial Improvements to County‐Level Fire Weather Forecasting Over the Western United States

Son.

Wang

et al. 2022

J Adv Model Earth Syst

View full text Add to dashboard Cite

show abstract

“…In today's time multimedia based applications are increasing day by day. Super resolution is also involving in multimedia industry [18]. In today's time movies, animations, visual effects all need the HD data.…”

Section: Sr In Multimedia Industry and Video Enhancementmentioning

confidence: 99%

Super Resolution Applications in Modern Digital Image Processing

Singh¹,

Sidhu²

2016

IJCA

View full text Add to dashboard Cite

This paper gives the various applications of Super Resolution (SR) in today's time modern digital image processing. It has been seen that SR implementation results are always increasing the performance of digital image processing techniques. Modern digital communication includes many applications which generate the data in the form of images and videos. Along with the other techniques of processing of images and videos, Super resolution of images has also grown. In the world of powerful processors and advanced display systems, SR has shown its existence and an overview of various applications based on super resolution has been given in this paper.

show abstract

“…To overcome this problem, the image processing community is developing a collection of algorithms known as super-resolution for generating high-resolution imagery from systems having lower-resolution imaging detectors. In 2001 and 2002 preliminary attempts were made to adapt SR algorithms to medical imaginary applications [4]. Early research deals with the magnetic resonance imaging.…”

Section: Super-resolution Image Reconstructionmentioning

confidence: 99%

Semi-PROPELLER Compressed Sensing Image Reconstruction with Enhanced Resolution in MRI

Malczewski¹

2015

International Journal of Electronics and Telecommunications

View full text Add to dashboard Cite

Abstract-Magnetic Resonance Imaging (MRI) reconstruction algorithm using semi-PROPELLER compressed sensing is presented in this paper. It is exhibited that introduced algorithm for estimating data shifts is feasible when super-resolution is applied. The offered approach utilizes compressively sensed MRI PROPELLER sequences and improves MR images spatial resolution in circumstances when highly undersampled k-space trajectories are applied. Compressed sensing (CS) aims at signal and images reconstructing from significantly fewer measurements than were traditionally thought necessary. It is shown that the presented approach improves MR spatial resolution in cases when Compressed Sensing (CS) sequences are used. The application of CS in medical modalities has the potential for significant scan time reductions, with visible benefits for patients and health care economics. These methods emphasize on maximizing image sparsity on known sparse transform domain and minimizing fidelity. This diagnostic modality struggles with an inherently slow data acquisition process. The use of CS to MRI leads to substantial scan time reductions [7] and visible benefits for patients and economic factors. In this report the objective is to combine Super-Resolution image enhancement algorithm with both PROPELLER sequence and CS framework. The motion estimation algorithm being a part of super resolution reconstruction (SRR) estimates shifts for all blades jointly, utilizing blade-pair correlations that are both strong and more robust to noise.

show abstract

Super Resolution for Multimedia, Image, and Video Processing Applications

Cited by 17 publications

References 54 publications

Deep Learning Provides Substantial Improvements to County‐Level Fire Weather Forecasting Over the Western United States

Deep Learning Provides Substantial Improvements to County‐Level Fire Weather Forecasting Over the Western United States

Super Resolution Applications in Modern Digital Image Processing

Semi-PROPELLER Compressed Sensing Image Reconstruction with Enhanced Resolution in MRI

Contact Info

Product

Resources

About