Inverse Scale Spaces for Nonlinear Regularization

2006 ◽  
Vol 27 (1) ◽  
pp. 41-50 ◽  
Author(s):  
Johan Lie ◽  
Jan M. Nordbotten
Keyword(s):  
Nonlinear Regularization ◽  
Scale Spaces

scholarly journals A Lightweight Fusion Distillation Network for Image Deblurring and Deraining

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5312
Author(s):  
Yanni Zhang ◽  
Yiming Liu ◽  
Qiang Li ◽  
Jianzhong Wang ◽  
Miao Qi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Deblurring ◽  
Image Features ◽  
Image Feature ◽  
Model Complexity ◽  
Small Scale ◽  
Feature Maps ◽  
Learning Framework ◽  
Channel Information ◽  
Scale Spaces

Recently, deep learning-based image deblurring and deraining have been well developed. However, most of these methods fail to distill the useful features. What is more, exploiting the detailed image features in a deep learning framework always requires a mass of parameters, which inevitably makes the network suffer from a high computational burden. We propose a lightweight fusion distillation network (LFDN) for image deblurring and deraining to solve the above problems. The proposed LFDN is designed as an encoder–decoder architecture. In the encoding stage, the image feature is reduced to various small-scale spaces for multi-scale information extraction and fusion without much information loss. Then, a feature distillation normalization block is designed at the beginning of the decoding stage, which enables the network to distill and screen valuable channel information of feature maps continuously. Besides, an information fusion strategy between distillation modules and feature channels is also carried out by the attention mechanism. By fusing different information in the proposed approach, our network can achieve state-of-the-art image deblurring and deraining results with a smaller number of parameters and outperform the existing methods in model complexity.

scholarly journals Infrared and visible image fusion via octave Gaussian pyramid framework

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lei Yan ◽  
Qun Hao ◽  
Jie Cao ◽  
Rizvi Saad ◽  
Kun Li ◽  
...  
Keyword(s):  
Image Fusion ◽  
Low Frequency ◽  
Objective Evaluation ◽  
Visible Image ◽  
Composite Image ◽  
Multiscale Decomposition ◽  
Gaussian Pyramid ◽  
Fusion Methods ◽  
Scale Spaces ◽  
Fusion Framework

AbstractImage fusion integrates information from multiple images (of the same scene) to generate a (more informative) composite image suitable for human and computer vision perception. The method based on multiscale decomposition is one of the commonly fusion methods. In this study, a new fusion framework based on the octave Gaussian pyramid principle is proposed. In comparison with conventional multiscale decomposition, the proposed octave Gaussian pyramid framework retrieves more information by decomposing an image into two scale spaces (octave and interval spaces). Different from traditional multiscale decomposition with one set of detail and base layers, the proposed method decomposes an image into multiple sets of detail and base layers, and it efficiently retains high- and low-frequency information from the original image. The qualitative and quantitative comparison with five existing methods (on publicly available image databases) demonstrate that the proposed method has better visual effects and scores the highest in objective evaluation.

scholarly journals Boundary Morphology for Hierarchical Simplification of Archaeological Fragments

2020 ◽  
Vol 4 (1) ◽  
pp. 46-63
Author(s):  
Hanan ElNaghy ◽  
Leo Dorst
Keyword(s):  
Mathematical Morphology ◽  
Masking Effect ◽  
Morphological Operations ◽  
Fracture Surfaces ◽  
Morphological Approach ◽  
Archaeological Artifacts ◽  
Scale Spaces ◽  
Extrusion Method

AbstractWhen fitting archaeological artifacts, one would like to have a representation that simplifies fragments while preserving their complementarity. In this paper, we propose to employ the scale-spaces of mathematical morphology to hierarchically simplify potentially fitting fracture surfaces. We study the masking effect when morphological operations are applied to selected subsets of objects. Since fitting locally depends on the complementarity of fractures only, we introduce ‘Boundary Morphology’ on surfaces rather than volumes. Moreover, demonstrating the Lipschitz nature of the terracotta fractures informs our novel extrusion method to compute both closing and opening operations simultaneously. We also show that in this proposed representation the effects of abrasion and uncertainty are naturally bounded, justifying the morphological approach. This work is an extension of our contribution earlier published in the proceedings of ISMM2019 [10].

Sparsification Scale-Spaces

2019 ◽  
pp. 303-314
Author(s):  
Marcelo Cárdenas ◽  
Pascal Peter ◽  
Joachim Weickert
Keyword(s):  
Scale Spaces

Instruction of verbal and spatial strategies for the learning about large-scale spaces

2014 ◽  
Vol 35 ◽  
pp. 15-21 ◽  
Author(s):  
Wen Wen ◽  
Toru Ishikawa ◽  
Takao Sato
Keyword(s):  
Large Scale ◽  
Spatial Strategies ◽  
Scale Spaces

scholarly journals Automatic diatom identification using contour analysis by morphological curvature scale spaces

2005 ◽  
Vol 16 (4) ◽  
pp. 217-228 ◽  
Author(s):  
Andrei C. Jalba ◽  
Michael H. F. Wilkinson ◽  
Jos B. T. M. Roerdink ◽  
Micha M. Bayer ◽  
Stephen Juggins
Keyword(s):  
Contour Analysis ◽  
Scale Spaces

Retention of memory for large-scale spaces

Memory ◽  
2013 ◽  
Vol 21 (7) ◽  
pp. 807-817 ◽  
Author(s):  
Toru Ishikawa
Keyword(s):  
Large Scale ◽  
Scale Spaces

Image Features from Morphological Scale-Spaces

2011 ◽  
pp. 32-79
Author(s):  
Sébastien Lefèvre
Keyword(s):  
Data Mining ◽  
Performance Evaluation ◽  
Video Processing ◽  
State Of The Art ◽  
Image Features ◽  
Video Data ◽  
Multimedia Data ◽  
Multimedia Data Mining ◽  
Shot Detection ◽  
Scale Spaces

Video processing and segmentation are important stages for multimedia data mining, especially with the advance and diversity of video data available. The aim of this chapter is to introduce researchers, especially new ones, to the “video representation, processing, and segmentation techniques”. This includes an easy and smooth introduction, followed by principles of video structure and representation, and then a state-of-the-art of the segmentation techniques focusing on the shot-detection. Performance evaluation and common issues are also discussed before concluding the chapter.

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