liu.seSearch for publications in DiVA
Endre søk
Begrens søket
1 - 11 of 11
RefereraExporteraLink til resultatlisten
Permanent link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Treff pr side
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sortering
  • Standard (Relevans)
  • Forfatter A-Ø
  • Forfatter Ø-A
  • Tittel A-Ø
  • Tittel Ø-A
  • Type publikasjon A-Ø
  • Type publikasjon Ø-A
  • Eldste først
  • Nyeste først
  • Skapad (Eldste først)
  • Skapad (Nyeste først)
  • Senast uppdaterad (Eldste først)
  • Senast uppdaterad (Nyeste først)
  • Disputationsdatum (tidligste først)
  • Disputationsdatum (siste først)
  • Standard (Relevans)
  • Forfatter A-Ø
  • Forfatter Ø-A
  • Tittel A-Ø
  • Tittel Ø-A
  • Type publikasjon A-Ø
  • Type publikasjon Ø-A
  • Eldste først
  • Nyeste først
  • Skapad (Eldste først)
  • Skapad (Nyeste først)
  • Senast uppdaterad (Eldste først)
  • Senast uppdaterad (Nyeste først)
  • Disputationsdatum (tidligste først)
  • Disputationsdatum (siste først)
Merk
Maxantalet träffar du kan exportera från sökgränssnittet är 250. Vid större uttag använd dig av utsökningar.
  • 1.
    Baravdish, George
    et al.
    Linköpings universitet, Institutionen för teknik och naturvetenskap, Kommunikations- och transportsystem. Linköpings universitet, Tekniska högskolan.
    Svensson, Olof
    Linköpings universitet, Institutionen för teknik och naturvetenskap, Kommunikations- och transportsystem. Linköpings universitet, Tekniska högskolan.
    Åström, Freddie
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    On Backward p(x)-Parabolic Equations for Image Enhancement2015Inngår i: Numerical Functional Analysis and Optimization, ISSN 0163-0563, E-ISSN 1532-2467, Vol. 36, nr 2, s. 147-168Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In this study, we investigate the backward p(x)-parabolic equation as a new methodology to enhance images. We propose a novel iterative regularization procedure for the backward p(x)-parabolic equation based on the nonlinear Landweber method for inverse problems. The proposed scheme can also be extended to the family of iterative regularization methods involving the nonlinear Landweber method. We also investigate the connection between the variable exponent p(x) in the proposed energy functional and the diffusivity function in the corresponding Euler-Lagrange equation. It is well known that the forward problems converges to a constant solution destroying the image. The purpose of the approach of the backward problems is twofold. First, solving the backward problem by a sequence of forward problems we obtain a smooth image which is denoised. Second, by choosing the initial data properly we try to reduce the blurriness of the image. The numerical results for denoising appear to give improvement over standard methods as shown by preliminary results.

  • 2.
    Freddie, Åström
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Michael, Felsberg
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Reiner, Lenz
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Institutionen för teknik och naturvetenskap, Medie- och Informationsteknik. Linköpings universitet, Tekniska högskolan.
    Color Persistent Anisotropic Diffusion of Images2011Inngår i: Image Analysis / [ed] Anders Heyden, Fredrik Kahl, Heidelberg: Springer, 2011, s. 262-272Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Techniques from the theory of partial differential equations are often used to design filter methods that are locally adapted to the image structure. These techniques are usually used in the investigation of gray-value images. The extension to color images is non-trivial, where the choice of an appropriate color space is crucial. The RGB color space is often used although it is known that the space of human color perception is best described in terms of non-euclidean geometry, which is fundamentally different from the structure of the RGB space. Instead of the standard RGB space, we use a simple color transformation based on the theory of finite groups. It is shown that this transformation reduces the color artifacts originating from the diffusion processes on RGB images. The developed algorithm is evaluated on a set of real-world images, and it is shown that our approach exhibits fewer color artifacts compared to state-of-the-art techniques. Also, our approach preserves details in the image for a larger number of iterations.

  • 3.
    Heinemann, Christian
    et al.
    Forschungszentrum Jülich, Germany.
    Åström, Freddie
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Baravdish, George
    Linköpings universitet, Institutionen för teknik och naturvetenskap, Kommunikations- och transportsystem. Linköpings universitet, Tekniska högskolan.
    Krajsek, Kai
    Forschungszentrum Jülich, Germany.
    Felsberg, Michael
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Scharr, Hanno
    Forschungszentrum Jülich, Germany.
    Using Channel Representations in Regularization Terms: A Case Study on Image Diffusion2014Inngår i: Proceedings of the 9th International Conference on Computer Vision Theory and Applications, SciTePress, 2014, Vol. 1, s. 48-55Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In this work we propose a novel non-linear diffusion filtering approach for images based on their channel representation. To derive the diffusion update scheme we formulate a novel energy functional using a soft-histogram representation of image pixel neighborhoods obtained from the channel encoding. The resulting Euler-Lagrange equation yields a non-linear robust diffusion scheme with additional weighting terms stemming from the channel representation which steer the diffusion process. We apply this novel energy formulation to image reconstruction problems, showing good performance in the presence of mixtures of Gaussian and impulse-like noise, e.g. missing data. In denoising experiments of common scalar-valued images our approach performs competitive compared to other diffusion schemes as well as state-of-the-art denoising methods for the considered noise types.

  • 4.
    Åström, Freddie
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Tekniska högskolan.
    A Variational Approach to Image Diffusion in Non-Linear Domains2013Licentiatavhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    Image filtering methods are designed to enhance noisy images captured in situations that are problematic for the camera sensor. Such noisy images originate from unfavourable illumination conditions, camera motion, or the desire to use only a low dose of ionising radiation in medical imaging. Therefore, in this thesis work I have investigated the theory of partial differential equations (PDE) to design filtering methods that attempt to remove noise from images. This is achieved by modeling and deriving energy functionals which in turn are minimized to attain a state of minimum energy. This state is obtained by solving the so called Euler-Lagrange equation. An important theoretical contribution of this work is that conditions are put forward determining when a PDE has a corresponding energy functional. This is in particular described in the case of the structure tensor, a commonly used tensor in computer vision.A primary component of this thesis work is to model adaptive image filtering such that any modification of the image is structure preserving, but yet is noise suppressing. In color image filtering this is a particular challenge since artifacts may be introduced at color discontinuities. For this purpose a non-Euclidian color opponent transformation has been analysed and used to separate the standard RGB color space into uncorrelated components.A common approach to achieve adaptive image filtering is to select an edge stopping function from a set of functions that have proven to work well in the past. The purpose of the edge stopping function is to inhibit smoothing of image features that are desired to be retained, such as lines, edges or other application dependent characteristics. Thus, a step from ad-hoc filtering based on experience towards an application-driven filtering is taken, such that only desired image features are processed. This improves what is characterised as visually relevant features, a topic which this thesis covers, in particular for medical imaging.The notion of what are relevant features is a subjective measure may be different from a layman's opinion compared to a professional's. Therefore, we advocate that any image filtering method should yield an improvement not only in numerical measures but also a visual improvement should be experienced by the respective end-user

    Delarbeid
    1. Color Persistent Anisotropic Diffusion of Images
    Åpne denne publikasjonen i ny fane eller vindu >>Color Persistent Anisotropic Diffusion of Images
    2011 (engelsk)Inngår i: Image Analysis / [ed] Anders Heyden, Fredrik Kahl, Heidelberg: Springer, 2011, s. 262-272Konferansepaper, Publicerat paper (Fagfellevurdert)
    Abstract [en]

    Techniques from the theory of partial differential equations are often used to design filter methods that are locally adapted to the image structure. These techniques are usually used in the investigation of gray-value images. The extension to color images is non-trivial, where the choice of an appropriate color space is crucial. The RGB color space is often used although it is known that the space of human color perception is best described in terms of non-euclidean geometry, which is fundamentally different from the structure of the RGB space. Instead of the standard RGB space, we use a simple color transformation based on the theory of finite groups. It is shown that this transformation reduces the color artifacts originating from the diffusion processes on RGB images. The developed algorithm is evaluated on a set of real-world images, and it is shown that our approach exhibits fewer color artifacts compared to state-of-the-art techniques. Also, our approach preserves details in the image for a larger number of iterations.

    sted, utgiver, år, opplag, sider
    Heidelberg: Springer, 2011
    Serie
    Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 6688
    Emneord
    Non-linear diffusion, color image processing, perceptual image quality
    HSV kategori
    Identifikatorer
    urn:nbn:se:liu:diva-68999 (URN)10.1007/978-3-642-21227-7_25 (DOI)978-3-642-21226-0 (ISBN)978-3-642-21227-7 (ISBN)
    Konferanse
    The 17th Scandinavian Conference on Image Analysis, 23-27 May 2011, Ystad Sweden
    Merknad

    Original Publication: Åström Freddie, Felsberg Michael and Lenz Reiner, Color Persistent Anisotropic Diffusion of Images, 2011, Image Analysis, SCIA conference, 23-27 May 2011, Ystad Sweden, 262-272. http://dx.doi.org/10.1007/978-3-642-21227-7_25 Copyright: Springer

    Tilgjengelig fra: 2011-06-17 Laget: 2011-06-15 Sist oppdatert: 2018-02-06bibliografisk kontrollert
    2. On Tensor-Based PDEs and their Corresponding Variational Formulations with Application to Color Image Denoising
    Åpne denne publikasjonen i ny fane eller vindu >>On Tensor-Based PDEs and their Corresponding Variational Formulations with Application to Color Image Denoising
    2012 (engelsk)Konferansepaper, Publicerat paper (Fagfellevurdert)
    Abstract [en]

    The case when a partial differential equation (PDE) can be considered as an Euler-Lagrange (E-L) equation of an energy functional, consisting of a data term and a smoothness term is investigated. We show the necessary conditions for a PDE to be the E-L equation for a corresponding functional. This energy functional is applied to a color image denoising problem and it is shown that the method compares favorably to current state-of-the-art color image denoising techniques.

    sted, utgiver, år, opplag, sider
    Springer Berlin/Heidelberg, 2012
    Serie
    Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 7574
    HSV kategori
    Identifikatorer
    urn:nbn:se:liu:diva-79603 (URN)10.1007/978-3-642-33712-3_16 (DOI)978-3-642-33711-6 (ISBN)978-3-642-33712-3 (ISBN)
    Konferanse
    ECCV 2012: 12th European Conference on Computer Vision, 7-12 October, Firenze, Italy
    Prosjekter
    NACIPGARNICSELLIIT
    Tilgjengelig fra: 2012-08-10 Laget: 2012-08-10 Sist oppdatert: 2017-06-01bibliografisk kontrollert
    3. Targeted Iterative Filtering
    Åpne denne publikasjonen i ny fane eller vindu >>Targeted Iterative Filtering
    2013 (engelsk)Konferansepaper, Publicerat paper (Fagfellevurdert)
    Abstract [en]

    The assessment of image denoising results depends on the respective application area, i.e. image compression, still-image acquisition, and medical images require entirely different behavior of the applied denoising method. In this paper we propose a novel, nonlinear diffusion scheme that is derived from a linear diffusion process in a value space determined by the application. We show that application-driven linear diffusion in the transformed space compares favorably with existing nonlinear diffusion techniques. 

    sted, utgiver, år, opplag, sider
    Springer Berlin/Heidelberg, 2013
    Serie
    Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 7893
    HSV kategori
    Identifikatorer
    urn:nbn:se:liu:diva-89674 (URN)10.1007/978-3-642-38267-3_1 (DOI)978-3-642-38266-6 (ISBN)978-3-642-38267-3 (ISBN)
    Konferanse
    Fourth International Conference on Scale Space and Variational Methods in Computer Vision (SSVM 2013), 2-6 June 2013, Schloss Seggau, Graz region, Austria
    Prosjekter
    VIDIGARNICSSM10-002BILDLAB
    Tilgjengelig fra: 2013-04-03 Laget: 2013-03-01 Sist oppdatert: 2018-01-26bibliografisk kontrollert
  • 5.
    Åström, Freddie
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Tekniska högskolan.
    Variational Tensor-Based Models for Image Diffusion in Non-Linear Domains2015Doktoravhandling, monografi (Annet vitenskapelig)
    Abstract [en]

    This dissertation addresses the problem of adaptive image filtering.

    Although the topic has a long history in the image processing community, researchers continuously present novel methods to obtain ever better image restoration results.

    With an expanding market for individuals who wish to share their everyday life on social media, imaging techniques such as compact cameras and smart phones are important factors. Naturally, every producer of imaging equipment desires to exploit cheap camera components while supplying high quality images. One step in this pipeline is to use sophisticated imaging software including, e.g., noise reduction to reduce manufacturing costs, while maintaining image quality.

    This thesis is based on traditional formulations such as isotropic and tensor-based anisotropic diffusion for image denoising. The difference from main-stream denoising methods is that this thesis explores the effects of introducing contextual information as prior knowledge for image denoising into the filtering schemes. To achieve this, the adaptive filtering theory is formulated from an energy minimization standpoint. The core contributions of this work is the introduction of a novel tensor-based functional which unifies and generalises standard diffusion methods. Additionally, the explicit Euler-Lagrange equation is derived which, if solved, yield the stationary point for the minimization problem. Several aspects of the functional are presented in detail which include, but are not limited to, tensor symmetry constraints and convexity. Also, the classical problem of finding a variational formulation to a given tensor-based partial differential equation is studied.

    The presented framework is applied in problem formulation that includes non-linear domain transformation, e.g., visualization of medical images.

    Additionally, the framework is also used to exploit locally estimated probability density functions or the channel representation to drive the filtering process.

    Furthermore, one of the first truly tensor-based formulations of total variation is presented. The key to the formulation is the gradient energy tensor, which does not require spatial regularization of its tensor components. It is shown empirically in several computer vision applications, such as corner detection and optical flow, that the gradient energy tensor is a viable replacement for the commonly used structure tensor. Moreover, the gradient energy tensor is used in the traditional tensor-based anisotropic diffusion scheme. This approach results in significant improvements in computational speed when the scheme is implemented on a graphical processing unit compared to using the commonly used structure tensor.

  • 6.
    Åström, Freddie
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Baravdish, George
    Linköpings universitet, Institutionen för teknik och naturvetenskap, Kommunikations- och transportsystem. Linköpings universitet, Tekniska högskolan.
    Felsberg, Michael
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    A Tensor Variational Formulation of Gradient Energy Total Variation2015Inngår i: ENERGY MINIMIZATION METHODS IN COMPUTER VISION AND PATTERN RECOGNITION, EMMCVPR 2015, Springer Berlin/Heidelberg, 2015, Vol. 8932, s. 307-320Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We present a novel variational approach to a tensor-based total variation formulation which is called gradient energy total variation, GETV. We introduce the gradient energy tensor into the GETV and show that the corresponding Euler-Lagrange (E-L) equation is a tensor-based partial differential equation of total variation type. Furthermore, we give a proof which shows that GETV is a convex functional. This approach, in contrast to the commonly used structure tensor, enables a formal derivation of the corresponding E-L equation. Experimental results suggest that GETV compares favourably to other state of the art variational denoising methods such as extended anisotropic diffusion (EAD) and total variation (TV) for gray-scale and colour images.

  • 7.
    Åström, Freddie
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Baravdish, George
    Linköpings universitet, Institutionen för teknik och naturvetenskap, Kommunikations- och transportsystem. Linköpings universitet, Tekniska högskolan.
    Felsberg, Michael
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    On Tensor-Based PDEs and their Corresponding Variational Formulations with Application to Color Image Denoising2012Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The case when a partial differential equation (PDE) can be considered as an Euler-Lagrange (E-L) equation of an energy functional, consisting of a data term and a smoothness term is investigated. We show the necessary conditions for a PDE to be the E-L equation for a corresponding functional. This energy functional is applied to a color image denoising problem and it is shown that the method compares favorably to current state-of-the-art color image denoising techniques.

  • 8.
    Åström, Freddie
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Tekniska fakulteten.
    Felsberg, Michael
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Tekniska fakulteten.
    On the Choice of Tensor Estimation for Corner Detection, Optical Flow and Denoising2015Inngår i: COMPUTER VISION - ACCV 2014 WORKSHOPS, PT II / [ed] C.V. Jawahar and Shiguang Shan, Springer, 2015, Vol. 9009, s. 15s. 16-30Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Many image processing methods such as corner detection,optical flow and iterative enhancement make use of image tensors. Generally, these tensors are estimated using the structure tensor. In this work we show that the gradient energy tensor can be used as an alternativeto the structure tensor in several cases. We apply the gradient energy tensor to common image problem applications such as corner detection, optical flow and image enhancement. Our experimental results suggest that the gradient energy tensor enables real-time tensor-based image enhancement using the graphical processing unit (GPU) and we obtain 40% increase of frame rate without loss of image quality.

  • 9.
    Åström, Freddie
    et al.
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Felsberg, Michael
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Baravdish, George
    Linköpings universitet, Institutionen för teknik och naturvetenskap, Kommunikations- och transportsystem. Linköpings universitet, Tekniska högskolan.
    Lundström, Claes
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för teknik och naturvetenskap, Medie- och Informationsteknik. Linköpings universitet, Tekniska högskolan.
    Targeted Iterative Filtering2013Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The assessment of image denoising results depends on the respective application area, i.e. image compression, still-image acquisition, and medical images require entirely different behavior of the applied denoising method. In this paper we propose a novel, nonlinear diffusion scheme that is derived from a linear diffusion process in a value space determined by the application. We show that application-driven linear diffusion in the transformed space compares favorably with existing nonlinear diffusion techniques. 

  • 10.
    Åström, Freddie
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Koker, Rasit
    Engineering Faculty Esentepe Kampus, Computer Engineering Department, Sakarya University, Turkey.
    A parallel neural network approach to prediction of Parkinson´s Disease2011Inngår i: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 38, nr 10, s. 12470-12474Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Recently the neural network based diagnosis of medical diseases has taken a great deal of attention. In this paper a parallel feed-forward neural network structure is used in the prediction of Parkinson’s Disease. The main idea of this paper is using more than a unique neural network to reduce the possibility of decision with error. The output of each neural network is evaluated by using a rule-based system for the final decision. Another important point in this paper is that during the training process, unlearned data of each neural network is collected and used in the training set of the next neural network. The designed parallel network system significantly increased the robustness of the prediction. A set of nine parallel neural networks yielded an improvement of 8.4% on the prediction of Parkinson’s Disease compared to a single unique network. Furthermore, it is demonstrated that the designed system, to some extent, deals with the problems of imbalanced data sets.

  • 11.
    Åström, Freddie
    et al.
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Zografos, Vasileios
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Felsberg, Michael
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Density Driven Diffusion2013Inngår i: 18th Scandinavian Conferences on Image Analysis, 2013, 2013, s. 718-730Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In this work we derive a novel density driven diffusion scheme for image enhancement. Our approach, called D3, is a semi-local method that uses an initial structure-preserving oversegmentation step of the input image.  Because of this, each segment will approximately conform to a homogeneous region in the image, allowing us to easily estimate parameters of the underlying stochastic process thus achieving adaptive non-linear filtering. Our method is capable of producing competitive results when compared to state-of-the-art methods such as non-local means, BM3D and tensor driven diffusion on both color and grayscale images.

1 - 11 of 11
RefereraExporteraLink til resultatlisten
Permanent link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf