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Paper Abstract and Keywords
Presentation 2026-08-28 13:10
[Invited Talk] Structure-Aware Convex Regularization for Hyperspectral Image Restoration
Shingo Takemoto (Science Tokyo) SIP2026-48
Abstract (in Japanese) (See Japanese page) 
(in English) This talk presents two convex regularization methods for hyperspectral (HS) image restoration, focusing on denoising and destriping. HS sensors divide limited photons across hundreds of narrow spectral bands, yielding images severely degraded by mixed noise. Restoring clean images from such observations is an ill-posed inverse problem essential for preserving the scientific value of HS data.

Total Variation (TV) regularization is widely used for HS image restoration by modeling piecewise smoothness along spatial and spectral dimensions. However, existing TV-based methods have two fundamental limitations: (1) Uniformity---spatially uniform penalties over-smooth fine details; and (2) Locality---reliance on adjacent pixel differences lacks robustness under heavy noise and structural artifacts.

To address Uniformity, we propose Graph-Aided Spatio-Spectral Total Variation (GASSTV), which constructs spatial and spectral graphs from observed data to adaptively modulate penalization strength, enabling mixed-noise removal while preserving fine details.

To overcome Locality, we propose Spatio-Spectral Structure Tensor Total Variation (S3TTV), which extends the structure tensor to the spatio-spectral domain. By evaluating nuclear norms of structure tensors within semi-local windows, S3TTV captures spatial coherence and spectral correlation simultaneously, achieving robustness against heavy mixed noise. Both methods are formulated as convex optimization problems and solved using primal-dual splitting algorithms.
Keyword (in Japanese) (See Japanese page) 
(in English) Optimization / Regularization / Hyperspectral image / Denoising / Recovery / Signal Processing / Total Variation / Graph  
Reference Info. IEICE Tech. Rep., vol. 126, no. 157, SIP2026-48, pp. 69-69, Aug. 2026.
Paper # SIP2026-48 
Date of Issue 2026-08-20 (SIP) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF SIP2026-48

Conference Information
Committee SIP  
Conference Date 2026-08-27 - 2026-08-28 
Place (in Japanese) (See Japanese page) 
Place (in English) Moriaki Ryokan 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Signal Processing, etc. 
Paper Information
Registration To SIP 
Conference Code 2026-08-SIP 
Language English (Japanese title is available) 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Structure-Aware Convex Regularization for Hyperspectral Image Restoration 
Sub Title (in English)  
Keyword(1) Optimization  
Keyword(2) Regularization  
Keyword(3) Hyperspectral image  
Keyword(4) Denoising  
Keyword(5) Recovery  
Keyword(6) Signal Processing  
Keyword(7) Total Variation  
Keyword(8) Graph  
1st Author's Name Shingo Takemoto  
1st Author's Affiliation Institute of Science Tokyo (Science Tokyo)
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Speaker Author-1 
Date Time 2026-08-28 13:10:00 
Presentation Time 35 minutes 
Registration for SIP 
Paper # SIP2026-48 
Volume (vol) vol.126 
Number (no) no.157 
Page p.69 
#Pages 1 
Date of Issue 2026-08-20 (SIP) 

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