A New Pansharpening Algorithm Based on Total Variation
By Frosti Palsson, Johannes R. Sveinsson, M. O. Ulfarsson. IEEE Geoscience and Remote Sensing Letters, 2014
361 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Media Technology
View Paper: https://www.semanticscholar.org/paper/a4a2a54a8c8986e2bba5ff511fa580228e87f25b
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Davide Cozzolino
- Luisa Verdoliva
- Giuseppe Scarpa
- José M. Bioucas-Dias
- M. O. Ulfarsson
- Jocelyn Chanussot
- Johannes R. Sveinsson
- Frosti Palsson
In Media Technology
- Jocelyn Chanussot
- Shutao Li
- Liangpei Zhang
- Naoto Yokoya
- José M. Bioucas-Dias
- Leyuan Fang
- Gemine Vivone
- Giuseppe Scarpa
Citations in the corpus
- A Multiscale and Multidepth Convolutional Neural Network for Remote Sensing Imagery Pan-Sharpening (2017, background): “algorithm [13], Bayesian posterior probability [14], adaptive regularization based on normalized Gaussian distribution [15], total variation operators [16], [17], and sparse reconstruction”
- A New Benchmark Based on Recent Advances in Multispectral Pansharpening: Revisiting Pansharpening With Classical and Emerging Pansharpening Methods (2021, methodology): “TOTAL VARIATION PANSHARPENING The cost function for the TV pansharpening method in [67] is given by the following TV-regularized least squares problem:”
- Target-Adaptive CNN-Based Pansharpening (2017, background): “or variational [26], [27] that get state-of-the-art results.”
- Hyperspectral Pansharpening: A Review (2015, background): “The works [20], [21], [22] are representative of the Bayesian and variational classes.”
- Multispectral and Hyperspectral Image Fusion Using a 3-D-Convolutional Neural Network (2017, methodology): “Apart from the CS and MRA methods, there are model-based methods, such as [2] and [3], and methods based on statistical inference, such as [4] and [5].”
- Pansharpening by Convolutional Neural Networks (2016, methodology): “Total variation is used in [28], with the pansharpened image obtained by constrained energy minimization.”
- A Critical Comparison Among Pansharpening Algorithms (2015, background): “Some works rely on total variation penalization terms [25], [26] and others on recent developments in sparse signal representations, or compressive sensing theory [27], [28].”