A non-local MRF model for heritage architectural image completion
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Citations
Markov random field-based image inpainting with direction structure distribution analysis for maintaining structure coherence
Exploiting Multi-Direction Features in MRF-Based Image Inpainting Approaches
Region Filling and Object Removal in Images using Criminisi Algorithm
References
Pyramid-based texture analysis/synthesis
A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors
Shift-map image editing
Image Completion Using Efficient Belief Propagation Via Priority Scheduling and Dynamic Pruning
P3 & Beyond: Solving Energies with Higher Order Cliques
Related Papers (5)
Frequently Asked Questions (13)
Q2. What is the main purpose of the method?
Apart from object removal and ruined wall reconstruction the authors also use their method for an interesting application known as background replacement.
Q3. How many ssds are used in the example?
For all the examples, the belief thresholds for pruning and confidence is set to −2ssd0 and −ssd0 respectively, where −ssd0 represents a predefined mediocre ssd between the patches.
Q4. what is the ssd between two vectors?
Identity of indiscernibles: ssd between two vector is equal to zero iff both the vectors are equal, i.e.,ssd(Li, Lj) = 0 ⇐⇒ i = j, ∀i, j.3.
Q5. What is the proof of sub-modularity and semi-metricity of the energy functions?
The proof of sub-modularity and semi-metricity of the energy functions also guarantees that popular move making algorithm α-β swap can be efficiently used to find the global minima of this energy with a constant approximation [2].
Q6. What is the definition of a smoothness term?
To enforce coherency in the completed image, the authors define a smoothness term such that overlapping region of neighboring labels have least sum of squared distance.
Q7. How do the authors solve the energy minimization problem?
The authors solve the energy minimization problem on a corresponding graph, where each random variable is represented as a node in the graph.
Q8. How many repetitions are used in the graph?
Since the image may contain many repetitive patterns which are prominent, thus the authors generate the top C offsets to capture varied repetitions (C = 10 was used in their experiments).
Q9. What is the definition of the image completion problem?
The authors define the image completion problem in a labeling problem framework where overlapping spatial positions in image can be considered as a set of sites and patches of size w × h sampled from source region can be considered as labels.
Q10. What is the cost of xi taking label Li?
Ei(xi = Li) = l∑m=0km(xim − Lim) 2. (2)In other words, data term measures the agreement between random variable xi and label Li in terms of sum of squared distance (ssd) of known pixels.
Q11. How do the authors find the similar patches?
In the process of repetition offset computation, the authors use Approximate Nearest Neighbour1 technique in order to find the most similar patches.
Q12. What are the datasets for their experiments?
The dataset for their experiments comprises of a large variety of images of Indian Heritage sites including Hampi, Konark, Golkonda Fort etc.
Q13. What is the definition of the labeling problem?
The labeling problem here is to find the optimal function f∗ : S → L. Optimality criteria is defined based on quality of the image completion.