Fast and Effective L0 Gradient Minimization by Region Fusion

Nguyen, Rang; Brown, Michael S.

doi:10.1109/iccv.2015.32

Cited by 44 publications

(82 citation statements)

References 26 publications

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“…5 by alternately solving two subproblems. By reviewing the stateof-the-art L 0 minimization [28] and subset selection techniques [13], we can provide a simple but efficient algorithm called, "Global-L 0 ", to solve the problem.…”

Section: Problem Formulationmentioning

confidence: 99%

“…λ ← 2λ 8: until λ > λ l 9: return V L 0 minimization To solve subproblem 6, we adopt the algorithm based on region fusion [28]. Here, we only need to perform one iteration with the fixed λ value.…”

Section: Global-l 0 Algorithmmentioning

confidence: 99%

“…In practice, for data having high noise, we choose higher values for λ l and λ g . One can also simply set λ l equal to the one in [28]. Additionally, if the user has a prior knowledge of the scene (the value of m), then one can choose an appropriate λ g value.…”

Section: Parameter Settingmentioning

confidence: 99%

See 2 more Smart Citations

Fast regularity-constrained plane fitting

Lin

Wang

et al. 2020

ISPRS Journal of Photogrammetry and Remote Sensing

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Section: Problem Formulationmentioning

confidence: 99%

Section: Global-l 0 Algorithmmentioning

confidence: 99%

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Fast regularity-constrained plane fitting

Lin

Wang

et al. 2020

ISPRS Journal of Photogrammetry and Remote Sensing

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“…[ZXJ14] used a weighted median filter to imitate the L 0 filter, which reduces the computation complexity per pixel from

O (r^{2})

O (r)

with r being the kernel size. Nguyen and Brown [NB15] presented an approach to minimize the L 0 gradient with improved runtime. They employed a region fusion method that unites regions of similar colour to obtain regions of constant colour.…”

Section: Related Workmentioning

confidence: 99%

Stylized Image Triangulation

Lawonn

Günther

2018

Computer Graphics Forum

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The art of representing images with triangles is known as image triangulation, which purposefully uses abstraction and simplification to guide the viewer's attention. The manual creation of image triangulations is tedious and thus several tools have been developed in the past that assist in the placement of vertices by means of image feature detection and subsequent Delaunay triangulation. In this paper, we formulate the image triangulation process as an optimization problem. We provide an interactive system that optimizes the vertex locations of an image triangulation to reduce the root mean squared approximation error. Along the way, the triangulation is incrementally refined by splitting triangles until certain refinement criteria are met. Thereby, the calculation of the energy gradients is expensive and thus we propose an efficient rasterization‐based GPU implementation. To ensure that artists have control over details, the system offers a number of direct and indirect editing tools that split, collapse and re‐triangulate selected parts of the image. For final display, we provide a set of rendering styles, including constant colours, linear gradients, tonal art maps and textures. Finally, we demonstrate temporal coherence for animations and compare our method with existing image triangulation tools.

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“…In WLS, a regularization term is added to minimize the horizontal and vertical gradients with corresponding smoothness weights. Recently, models based on l 0 -gradient minimization [24][25][26][27][28][29] have become popular. These models focus on the l 0 -norm of the image gradients.…”

Section: Introductionmentioning

confidence: 99%

A Convex Model for Edge-Histogram Specification with Applications to Edge-Preserving Smoothing

Chan

Nikolova

2018

Axioms

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Abstract:The goal of edge-histogram specification is to find an image whose edge image has a histogram that matches a given edge-histogram as much as possible. Mignotte has proposed a non-convex model for the problem in 2012. In his work, edge magnitudes of an input image are first modified by histogram specification to match the given edge-histogram. Then, a non-convex model is minimized to find an output image whose edge-histogram matches the modified edge-histogram. The non-convexity of the model hinders the computations and the inclusion of useful constraints such as the dynamic range constraint. In this paper, instead of considering edge magnitudes, we directly consider the image gradients and propose a convex model based on them. Furthermore, we include additional constraints in our model based on different applications. The convexity of our model allows us to compute the output image efficiently using either Alternating Direction Method of Multipliers or Fast Iterative Shrinkage-Thresholding Algorithm. We consider several applications in edge-preserving smoothing including image abstraction, edge extraction, details exaggeration, and documents scan-through removal. Numerical results are given to illustrate that our method successfully produces decent results efficiently.

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Fast and Effective L0 Gradient Minimization by Region Fusion

Cited by 44 publications

References 26 publications

Fast regularity-constrained plane fitting

Fast regularity-constrained plane fitting

Stylized Image Triangulation

A Convex Model for Edge-Histogram Specification with Applications to Edge-Preserving Smoothing

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