Structure Adaptive Total Variation Minimization-Based Image Decomposition

Song, Jinjoo; Cho, Heeryon; Yoon, Jungho; Yoon, Sang Min

doi:10.1109/tcsvt.2017.2717542

Cited by 29 publications

(19 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However the performance of STTC specifically for image/ video transmission can be significantly enhanced by leveraging the bounded variation property [28] that is a fundamental characteristic of multimedia signals. TV regularization based image/ video reconstruction schemes that exploit the BV property have gained popularity for several image/ video processing applications such as noise removal [29], deblurring [30]- [32], interpolation [33], inpainting [34], [35], super resolution (SR) based reconstruction [36]- [39], structure decomposition [40] etc. This is due to the edge preserving property [41] of the l 1 norm based anisotropic TV regularization term that can lead to a significant improvement in reconstruction quality [42].…”

Section: A Review Of Work In Existing Literaturementioning

confidence: 99%

Hierarchical Trellis Based Decoder for Total Variation Sequence Detection (TVSD) in Space-Time Trellis Coded (STTC) Wireless Image/Video Communication

et al. 2020

View full text Add to dashboard Cite

This paper proposes a robust reconstruction scheme for image/ video transmission in space-time trellis coding (STTC) based MIMO wireless multimedia sensor networks (WMSN). The information bits of the image/ video stream are modulated using STTC prior to transmission over the MIMO wireless channel between the multimedia sensors and the cluster head. At the receiver, a novel total variation sequence detector (TVSD) is developed that employs an anisotropic total variation (TV) regularized cost function to leverage the bounded variation property of the image/ video stream, in addition to the coding and diversity advantages of the STTC, for improved image/ video recovery. For reconstruction, a novel bi-layered hierarchical trellis based modified Viterbi decoder is developed to decode the optimal bit sequence corresponding to the least TV cost function using appropriately modified node and branch metrics. Simulation results using several test images and video sequences demonstrate the improved reconstruction performance of the proposed scheme, especially in the low SNR regime (0 − 10 dB), in comparison to the conventional maximum likelihood (ML) decoder, which makes it ideally suited for implementation in practical WMSN. INDEX TERMS Image/video communication, MIMO, space-time trellis codes, total variation, Viterbi algorithm, wireless multimedia sensor networks.

show abstract

Section: A Review Of Work In Existing Literaturementioning

confidence: 99%

Hierarchical Trellis Based Decoder for Total Variation Sequence Detection (TVSD) in Space-Time Trellis Coded (STTC) Wireless Image/Video Communication

et al. 2020

View full text Add to dashboard Cite

show abstract

“…) Fig. 4 Top: evolution of the energy ε(ρ (k) , σ (k) ) defined in (37), in function of iterations (k), concerning the test presented in Figure 8. Bottom: absolute value of the relative variation between two successive energy values.…”

Section: Alternating Majorization-minimization Formentioning

confidence: 99%

“…Most of the existing works are based on the "retinex theory" [21], which states that most of the slight brightness variations in an image are due to lighting, while reflectance is piecewise-constant (as for instance a Mondrian image). A variety of clusteringbased [13,36] or sparsity-enhancing methods [14,29,36,37] have been developed based on this theory. Among others, the work of Baron and Malik [4], which presents interesting results, stands on multiple priors to solve the fundamental ambiguity of shape-from-shading, that we aim at revoking in the multi-view context.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Variational Reflectance Estimation from Multi-view Images

et al. 2018

View full text Add to dashboard Cite

We tackle the problem of reflectance estimation from a set of multi-view images, assuming known geometry. The approach we put forward turns the input images into reflectance maps, through a robust variational method. The variational model comprises an image-driven fidelity term and a term which enforces consistency of the reflectance estimates with respect to each view. If illumination is fixed across the views, then reflectance estimation remains under-constrained: a regularization term, which ensures piecewise-smoothness of the reflectance, is thus used. Reflectance is parameterized in the image domain, rather than on the surface, which makes the numerical solution much easier, by resorting to an alternating majorization-minimization approach. Experiments on both synthetic and real-world datasets are carried out to validate the proposed strategy.

show abstract

“…By integrating the total generalized variation and Gabor wavelets, Liu [26] presented a new weighted TGV-Gabor model for the cartoon-texture decomposition. Through smoothing image, simultaneously preserving or enhancing image edges, Song et al [27] proposed structure adaptive total variation based image decomposition model. By means of learning a set of generic filters that can efficiently represent cartoon and texture type images, Zhang and Patel [28] presented novel convolutional sparse and low-rank coding based methods for cartoon and texture decomposition.…”

Section: Introductionmentioning

confidence: 99%

A Cartoon+Texture Image Decomposition Variational Model Based on Preserving the Local Geometric Characteristics

Zhang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

This paper is devoted to decomposing one given image into the cartoon and texture. The cartoon parts correspond to the main large objects in the image, and the textural parts contain fine scale details. Mathematically, these two components should belong to the different function spaces corresponding to different norms. In most variational models of image decomposition, the cartoon parts are described by the total variation norm and the texture parts are characterized by its dual norm. Using these methods, the cartoon and texture can be separated from the original image. Some structures, such as edges, are well preserved in the cartoon image. However, as far as we know, none of these methods considers the geometric characteristics information of the texture position in the separated cartoon images. That is, the geometrical information in those separated cartoon images may have been destroyed, which can be seen from the experiments in this paper. To maintain these geometrical characteristics, we introduce one smoothed vector field and let it approximate to the negative gradient direction of the cartoon image, and then for the smoothed vector field, we let it belong to the Lebesgue space, thus a new variational decomposition model is proposed. The corresponding alternating direction method is discussed in detail. Experimental results are reported to show the visual qualities compared with other methods.

show abstract

Structure Adaptive Total Variation Minimization-Based Image Decomposition

Cited by 29 publications

References 61 publications

Hierarchical Trellis Based Decoder for Total Variation Sequence Detection (TVSD) in Space-Time Trellis Coded (STTC) Wireless Image/Video Communication

Hierarchical Trellis Based Decoder for Total Variation Sequence Detection (TVSD) in Space-Time Trellis Coded (STTC) Wireless Image/Video Communication

Variational Reflectance Estimation from Multi-view Images

A Cartoon+Texture Image Decomposition Variational Model Based on Preserving the Local Geometric Characteristics

Contact Info

Product

Resources

About