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Paper Abstract and Keywords
Presentation 2024-03-14 11:00
[Invited Talk] Pixels to Precision: Passing into the Future of Super-Resolution Mastery
Supatta Viriyavisuthisakul (PIM) IMQ2023-42 IE2023-97 MVE2023-71
Abstract (in Japanese) (See Japanese page) 
(in English) Single Image Super-Resolution (SISR) involves reconstructing low-resolution images to enhance perceptual quality. Recent advancements in Generative Adversarial Networks (GANs), such as SRGAN and ESRGAN, have improved reconstruction results. However, challenges persist, including the generation of hallucinated details, undesirable artifacts, and prolonged convergence times. To address these issues, we propose incorporating four types of parametric regularization algorithms into the loss function of the model. These algorithms enable iterative weight adjustment of the network gradient, enhancing texture details and sharpness in reconstructed images. Experimental results demonstrate the effectiveness of our approach, achieving superior Image Quality Assessment (IQA) scores compared to previous works, while reducing training time. Furthermore, we extend our approach to Scene Text Image Super-Resolution (STISR), aiming to enhance the quality and text recognition accuracy of low-resolution scene text images. Within the Text ATTention network (TATT) framework, which integrates convolutional neural network (CNN) and transformer-based architectures, we introduce parametric regularization and weight parameters into the loss function. Unlike previous methods, our approach focuses solely on text information, addressing challenges related to improper-shaped texts and blurred text regions of the real-world dataset. Experimental comparisons with state-of-the-art STISR models demonstrate significant improvements across all proposed methods, yielding clearer and sharper edges and enhancing overall image quality. Our work represents a significant advancement in both single-image and scene text super-resolution.
Keyword (in Japanese) (See Japanese page) 
(in English) Super-resolution / Scene Text / Image Reconstruction / Trainable parameter / Parametric / Regularization / Parametric, Regularization /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 433, MVE2023-71, pp. 165-165, March 2024.
Paper # MVE2023-71 
Date of Issue 2024-03-06 (IMQ, IE, MVE) 
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 IMQ2023-42 IE2023-97 MVE2023-71

Conference Information
Committee IE MVE CQ IMQ  
Conference Date 2024-03-13 - 2024-03-15 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Sangyo Shien Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Media of five senses, Multimedia, Media experience, Picture codinge, Image media quality, Network,quality and reliability, etc（AC） 
Paper Information
Registration To MVE 
Conference Code 2024-03-IE-MVE-CQ-IMQ 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Pixels to Precision: Passing into the Future of Super-Resolution Mastery 
Sub Title (in English)  
Keyword(1) Super-resolution  
Keyword(2) Scene Text  
Keyword(3) Image Reconstruction  
Keyword(4) Trainable parameter  
Keyword(5) Parametric  
Keyword(6) Regularization  
Keyword(7) Parametric, Regularization  
Keyword(8)  
1st Author's Name Supatta Viriyavisuthisakul  
1st Author's Affiliation Panyapiwat Institute of Management (PIM)
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Speaker Author-1 
Date Time 2024-03-14 11:00:00 
Presentation Time 40 minutes 
Registration for MVE 
Paper # IMQ2023-42, IE2023-97, MVE2023-71 
Volume (vol) vol.123 
Number (no) no.430(IMQ), no.432(IE), no.433(MVE) 
Page p.165 
#Pages 1 
Date of Issue 2024-03-06 (IMQ, IE, MVE) 

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