Interactive Image Inpainting of Large-Scale Missing Region (original) (raw)
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Inpainting also known as retouching is the process by which we try to fill in the damaged or missing portions of an image in such a way that it is unable for the person seeing the image to find the fault within the image. Digital Image In painting, a relatively young research area is an art of filling in the missing or corrupted regions in an image using information from the neighboring pixels in a visually plausible manner, while restoring its unity. It is helpfully used for object removal in digital photographs, image reconstruction, text removal, video restoration, special effects in movie discussions and so on. There are numbers of method used for image inpainting. All methods have their own advantage and disadvantages. This paper presents a comparative study and review of different image in painting techniques. The algorithms are analyzed theoretically as well as experimentally.
Image Inpainting Algorithm for Region Filling Application
Image Inpainting is the process of reconstructing or retouching an image. It has a wide range of applications including region filling, object removal, denoising, compression, etc. It is an important work done in the field of image processing. This research work has been carried out to reconstruct an old image by filling the spatial information from the surroundings in the damaged portion such as scratches, tampered pages, missing areas and holes. This paper provides details regarding image inpainting using exemplar based method for both structure and texture reconstruction.
Review of Different Inpainting Algorithms
International Journal of Computer Applications, 2012
Image inpainting was historically done manually by painters for removing defect from paintings and photographs. Fill the region of missing information from a signal using surrounding information and re-form signal is the basic work of inpainting algorithms. Here in this paper we have studied and reviewed many different algorithms present for doing image inpainting and explain their approach. We have briefly explain some algorithms for video inpainting applications. This paper contain work done in the field of image inpainting and guide newcomers who are willing to work in image inpainting field.
Coherent Spatial and Colour Blended Exemplar Inpainting
Mehran University Research Journal of Engineering and Technology, 2017
In an image processing field the digital image recovery is termed as inpainting. Efficient retrieval of an image, especially having large objects with high curvature and complex texture is an immensely challenging problem for image inpainting researchers and practitioner. This enthused researchers and emerge various inpainting algorithms and many are in progress. Generally inpainting techniques approaches the available area source of given image(s) to restore the unavailable area target by the information available at the target edge. This paper represents a novel approach BSDD (Blended Spatial and Dimensional Distances) by sampling patches at each pixel of the source region. From the given sample, selection of local edge patch is gradient based without priority computation overhead as previous techniques. These local patches are searched globally by linear distance in which both spatial and dimensional distances are considered with regularization factor. The main motive of this method consists in achieving the efficiency, curvature and textural challenges of inpainting without compromising the quality of inpainted image. We have tested the proposed method in real as well as synthetic images with high curvature and complex textures in all cases results are comparable with other well-known techniques. In view of quality and optical the proposed algorithm exhibits better results.
A non-iterative automated mechanism for image inpainting
3rd IEEE International Conference on Adaptive Science and Technology (ICAST 2011), 2011
Portions of an image may be damaged or missing. Inpainting is required to recover the missing portions. Inpainting is cleaning off dirt, filling discolored sports, and repairing torn, warped, or cracked in a damaged image. The existing Navier-Stokes Partial Differential Equations (PDE) method for inpainting is iterative by nature, with a time variable serving as iteration parameter. For reasons of stability, a large number of iterations can be needed which results in a computational complexity that is often too large for interactive image manipulation. A non-iterative automated mechanism for image inpainting is proposed. Colors are treated as fluid that flow or diffuse from the surrounding areas into the empty region. Gains ranging from 9.44 dB to 19.49 dB were obtained with the non-iterative automated inpainting scheme. The automated inpainting scheme overcomes the computational complexity associated with the existing Navier-Stokes PDE inpainting method, and is more suitable for interactive image manipulations.
A comparison of image inpainting techniques
SPIE Proceedings, 2015
Image inpainting is the technique of reconstruction of the damaged image in an undetectable form. The goals and application of this technique are numerous, from the restoration of old damaged paintings and photograph to the removal or replacement of selected object in an image. This paper implements three algorithms for digital image inpainting. In this algorithm user selects the regions to be restored or filled and the algorithm automatically fills in these regions with information surrounding them. In method1 the pyramids of the image are generated to the level where all the damaged pixels are fully eliminated. Then the damaged pixels are filled in from the bottom of the pyramid to the top. In pyramid image at level i, the damaged pixels are filled in from the expanded pyramid image at level i − 1, and so on up to the level 0 pyramid. In method 2 the algorithm is iterative. In the first iteration, median value of known pixels' in each direction is calculated, and then, a damaged pixel is replaced by the median of the obtained values. In latter iterations, median of all pixels' values in each direction is calculated then median of obtained values is copied in place of the damaged pixel. The above algorithms are tested by applying different images and performance compared by using Signal to Noise Ratio (SNR).
Image inpainting by global structure and texture propagation
2007
Image inpainting is a technique to repair damaged images or modify images in a non-detectable form. In this paper, a novel global algorithm for region filling is proposed for image inpainting. After removing objects from an image, our approach fills the regions using patches taken from the image. The filling process is formulated as an energy minimization problem by Markov random fields (MRFs) and the belief propagation (BP) is utilized to solve the problem. Our energy function includes structure and texture information obtained from the image. One challenge in using BP is that its computational complexity is the square of the number of label candidates. To reduce the large number of label candidates, we present a coarse-to-fine scheme where two BPs run with much smaller numbers of label candidates instead of one BP running with a large number of label candidates. Experimental results demonstrate the excellent performance of our algorithm over other related algorithms.
Novel Approach for Image Inpainting
IJSRD, 2014
This presents a novel and efficient examplarbased inpainting algorithm through investigating the sparsity of natural image patches. Two novel concepts of sparsity at the patch level are proposed for modeling the patch priority and patch representation, which are two crucial steps for patch propagation in the examplar-based inpainting approach. First, patch structure sparsity is designed to measure the confidence of a patch located at the image structure the sparseness of its nonzero similarities to the neighboring patches. The patch with larger structure sparsity will be assigned higher priority for further inpainting. Second, it is assumed that the patch to be filled can be represented by the sparse linear combination of candidate patches under the local patch consistency constraint in a framework of sparse representation. Compared with the traditional examplar-based inpainting approach, structure sparsity enables better discrimination of structure and texture, and the patch sparse representation forces the newly inpainted regions to be sharp and consistent with the surrounding textures.
Survey on Different Techniques for Image Inpainting
Inpainting is derived from art restoration ,also called re-touching. Image Inpainting refers to the application of sophisticated algorithms to replace lost or corrupted parts of the image data. The main aim of inpainting is to fill scratches in the image or photos as well as to remove larger objects from them. The main objective of inpainting is to reconstruct the missing region in such a way that the observer does not comes to know that the image has been manipulated. Application of image inpainting include interpolation, photo restoration, zooming and super resolution, etc. In this paper several image inpainting techniques are explained with its pros and cons.