517 research outputs found
RIBBONS: Rapid Inpainting Based on Browsing of Neighborhood Statistics
Image inpainting refers to filling missing places in images using neighboring
pixels. It also has many applications in different tasks of image processing.
Most of these applications enhance the image quality by significant unwanted
changes or even elimination of some existing pixels. These changes require
considerable computational complexities which in turn results in remarkable
processing time. In this paper we propose a fast inpainting algorithm called
RIBBONS based on selection of patches around each missing pixel. This would
accelerate the execution speed and the capability of online frame inpainting in
video. The applied cost-function is a combination of statistical and spatial
features in all neighboring pixels. We evaluate some candidate patches using
the proposed cost function and minimize it to achieve the final patch.
Experimental results show the higher speed of 'Ribbons' in comparison with
previous methods while being comparable in terms of PSNR and SSIM for the
images in MISC dataset
Image Completion for View Synthesis Using Markov Random Fields and Efficient Belief Propagation
View synthesis is a process for generating novel views from a scene which has
been recorded with a 3-D camera setup. It has important applications in 3-D
post-production and 2-D to 3-D conversion. However, a central problem in the
generation of novel views lies in the handling of disocclusions. Background
content, which was occluded in the original view, may become unveiled in the
synthesized view. This leads to missing information in the generated view which
has to be filled in a visually plausible manner. We present an inpainting
algorithm for disocclusion filling in synthesized views based on Markov random
fields and efficient belief propagation. We compare the result to two
state-of-the-art algorithms and demonstrate a significant improvement in image
quality.Comment: Published version:
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=673843
Image Inpainting by Hyperbolic Selection of Pixels for Two Dimensional Bicubic Interpolations
Image inpainting is a restoration process which has numerous applications.
Restoring of scanned old images with scratches, or removing objects in images
are some of inpainting applications. Different approaches have been used for
implementation of inpainting algorithms. Interpolation approaches only consider
one direction for this purpose. In this paper we present a new perspective to
image inpainting. We consider multiple directions and apply both
one-dimensional and two-dimensional bicubic interpolations. Neighboring pixels
are selected in a hyperbolic formation to better preserve corner pixels. We
compare our work with recent inpainting approaches to show our superior
results
Geometry-Aware Face Completion and Editing
Face completion is a challenging generation task because it requires
generating visually pleasing new pixels that are semantically consistent with
the unmasked face region. This paper proposes a geometry-aware Face Completion
and Editing NETwork (FCENet) by systematically studying facial geometry from
the unmasked region. Firstly, a facial geometry estimator is learned to
estimate facial landmark heatmaps and parsing maps from the unmasked face
image. Then, an encoder-decoder structure generator serves to complete a face
image and disentangle its mask areas conditioned on both the masked face image
and the estimated facial geometry images. Besides, since low-rank property
exists in manually labeled masks, a low-rank regularization term is imposed on
the disentangled masks, enforcing our completion network to manage occlusion
area with various shape and size. Furthermore, our network can generate diverse
results from the same masked input by modifying estimated facial geometry,
which provides a flexible mean to edit the completed face appearance. Extensive
experimental results qualitatively and quantitatively demonstrate that our
network is able to generate visually pleasing face completion results and edit
face attributes as well
A novel image inpainting framework based on multilevel image pyramids
Image inpainting is the art of manipulating an image so that it is visually unrecognizable way. A considerable amount of research has been done in this area over the last few years. However, the state of art techniques does suffer from computational complexities and plausible results. This paper proposes a multi-level image pyramid-based image inpainting algorithm. The image inpainting algorithm starts with the coarsest level of the image pyramid and overpainting information is transferred to the subsequent levels until the bottom level gets inpainted. The search strategy used in the algorithm is based on hashing the coherent information in an image which makes the search fast and accurate. Also, the search space is constrained based on the propagated information thereby reducing the complexity of the algorithm. Compared to other inpainting methods; the proposed algorithm inpaints the target region with better plausibility and human vision conformation. Experimental results show that the proposed algorithm achieves better results as compared to other inpainting techniques
Image de-fencing framework with hybrid inpainting algorithm
Detection and removal of fences from digital images become essential when an important part of the scene turns to be occluded by such unwanted structures. Image de-fencing is challenging because manually marking fence boundaries is tedious and time-consuming. In this paper, a novel image de-fencing algorithm that effectively detects and removes fences with minimal user input is presented. The user is only requested to mark few fence pixels; then, color models are estimated and used to train Bayes classifier to segment the fence and the background. Finally, the fence mask is refined exploiting connected component analysis and morphological operators. To restore the occluded region, a hybrid inpainting algorithm is proposed that integrates exemplar-based technique with a pyramid-based interpolation approach. In contrast to previous solutions which work only for regular pattern fences, the proposed technique is able to remove both regular and irregular fences. A large number of experiments are carried out on a wide variety of images containing different types of fences demonstrating the effectiveness of the proposed approach. The proposed approach is also compared with state-of-the-art image de-fencing and inpainting techniques and showed convincing results. 2016, Springer-Verlag London.Scopu
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