176 research outputs found
Hybrid Spectral Denoising Transformer with Guided Attention
In this paper, we present a Hybrid Spectral Denoising Transformer (HSDT) for
hyperspectral image denoising. Challenges in adapting transformer for HSI arise
from the capabilities to tackle existing limitations of CNN-based methods in
capturing the global and local spatial-spectral correlations while maintaining
efficiency and flexibility. To address these issues, we introduce a hybrid
approach that combines the advantages of both models with a Spatial-Spectral
Separable Convolution (S3Conv), Guided Spectral Self-Attention (GSSA), and
Self-Modulated Feed-Forward Network (SM-FFN). Our S3Conv works as a lightweight
alternative to 3D convolution, which extracts more spatial-spectral correlated
features while keeping the flexibility to tackle HSIs with an arbitrary number
of bands. These features are then adaptively processed by GSSA which per-forms
3D self-attention across the spectral bands, guided by a set of learnable
queries that encode the spectral signatures. This not only enriches our model
with powerful capabilities for identifying global spectral correlations but
also maintains linear complexity. Moreover, our SM-FFN proposes the
self-modulation that intensifies the activations of more informative regions,
which further strengthens the aggregated features. Extensive experiments are
conducted on various datasets under both simulated and real-world noise, and it
shows that our HSDT significantly outperforms the existing state-of-the-art
methods while maintaining low computational overhead. Code is at https:
//github.com/Zeqiang-Lai/HSDT.Comment: ICCV 202
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
The implicit objective of the biennial "international - Traveling Workshop on
Interactions between Sparse models and Technology" (iTWIST) is to foster
collaboration between international scientific teams by disseminating ideas
through both specific oral/poster presentations and free discussions. For its
second edition, the iTWIST workshop took place in the medieval and picturesque
town of Namur in Belgium, from Wednesday August 27th till Friday August 29th,
2014. The workshop was conveniently located in "The Arsenal" building within
walking distance of both hotels and town center. iTWIST'14 has gathered about
70 international participants and has featured 9 invited talks, 10 oral
presentations, and 14 posters on the following themes, all related to the
theory, application and generalization of the "sparsity paradigm":
Sparsity-driven data sensing and processing; Union of low dimensional
subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph
sensing/processing; Blind inverse problems and dictionary learning; Sparsity
and computational neuroscience; Information theory, geometry and randomness;
Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?;
Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website:
http://sites.google.com/site/itwist1
A REVIEW ON MULTIPLE-FEATURE-BASED ADAPTIVE SPARSE REPRESENTATION (MFASR) AND OTHER CLASSIFICATION TYPES
A new technique Multiple-feature-based adaptive sparse representation (MFASR) has been demonstrated for Hyperspectral Images (HSI's) classification. This method involves mainly in four steps at the various stages. The spectral and spatial information reflected from the original Hyperspectral Images with four various features. A shape adaptive (SA) spatial region is obtained in each pixel region at the second step. The algorithm namely sparse representation has applied to get the coefficients of sparse for each shape adaptive region in the form of matrix with multiple features. For each test pixel, the class label is determined with the help of obtained coefficients. The performances of MFASR have much better classification results than other classifiers in the terms of quantitative and qualitative percentage of results. This MFASR will make benefit of strong correlations that are obtained from different extracted features and this make use of effective features and effective adaptive sparse representation. Thus, the very high classification performance was achieved through this MFASR technique
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