1,721,029 research outputs found
Low-carbohydrate diet and coronary heart disease in women.
No full textHalton and colleagues (Nov. 9 issue)report that low-carbohydrate diets are not associated with an increased risk of coronary heart
disease in women. We pooled data from our own studies evaluating the effect of lifestyle changes(Mediterranean-style diet and exercise) on body weight, lipid levels, and vascular inflammatory markers in 190 overweight women who were followed for up to 2 years.Women consuming a low-carbohydrate diet lost more weight than did those consuming a
high-carbohydrate diet and had better lipid and inflammatory profiles. Restriction of carbohydrates within a Mediterranean style
diet may be advantageous for weight loss
An Unsupervised CNN-Based Pansharpening Framework with Spectral-Spatial Fidelity Balance
No full textIn recent years, deep learning techniques for pansharpening multiresolution images have gained increasing interest. Due to the lack of ground truth data, most deep learning solutions rely on synthetic reduced-resolution data for supervised training. This approach has limitations due to the statistical mismatch between real full-resolution and synthetic reduced-resolution data, which affects the models’ generalization capacity. Consequently, there has been a shift towards unsupervised learning frameworks for pansharpening deep learning-based techniques. Unsupervised schemes require defining sophisticated loss functions with at least two components: one for spectral quality, ensuring consistency between the pansharpened image and the input multispectral component, and another for spatial quality, ensuring consistency between the output and the panchromatic input. Despite promising results, there has been limited investigation into the interaction and balance of these loss terms to ensure stability and accuracy. This work explores how unsupervised spatial and spectral consistency losses can be reliably combined preserving the outocome quality. By examining these interactions, we propose a general rule for balancing the two loss components to enhance the stability and performance of unsupervised pansharpening models. Experiments on three state-of-the-art algorithms using WorldView-3 images demonstrate that methods trained with the proposed framework achieve good performance in terms of visual quality and numerical indexes
Pansharpening by Convolutional Neural Networks in the Full Resolution Framework
No full textIn recent years, there has been a growing interest in deep learning-based pansharpening. Thus far, research has mainly focused on architectures. Nonetheless, model training is an equally important issue. A first problem is the absence of ground truths, unavoidable in pansharpening. This is often addressed by training networks in a reduced-resolution domain and using the original data as ground truth, relying on an implicit scale invariance assumption. However, on full-resolution images, results are often disappointing, suggesting such invariance not to hold. A further problem is the scarcity of training data, which causes a limited generalization ability and a poor performance on off-training-test images. In this article, we propose a full-resolution training framework for deep learning-based pansharpening. The framework is fully general and can be used for any deep learning-based pansharpening model. Training takes place in the high-resolution domain, relying only on the original data, thus avoiding any loss of information. To ensure spectral and spatial fidelity, a suitable two-component loss is defined. The spectral component enforces consistency between the pansharpened output and the low-resolution multispectral input. The spatial component, computed at high resolution, maximizes the local correlation between each pansharpened band and the panchromatic input. At testing time, the target-adaptive operating modality is adopted, achieving good generalization with a limited computational overhead. Experiments carried out on WorldView-3, WorldView-2, and GeoEye-1 images show that methods trained with the proposed framework guarantee a pretty good performance in terms of both full-resolution numerical indexes and visual quality
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