A Novel Framework for Image Inpainting

International Journal of Computer Trends and Technology (IJCTT) – Volume 14 Number 3 - Aug 2014

A Novel Framework for Image Inpainting Sharmila Shaik #1 , Sudhakar P *2, Shaik Khaja Mohiddin #3 #M.Tech (CSE), VVIT, Nambur, Guntur. A.P., India *Dept. Of CSE , VVIT, Nambur, Guntur., A.P.,India #Dept of CSE, VVIT, Nambur, Guntur., A.P.,India Abstract— an image can be modified in an undetectable form with the help of inpainting techniques which is an art from the ancient days itself. There are numerous applications and objectives which stand as examples for these kinds of techniques, as fact the main aim behind this technique is to bring back the damaged image to its originality which is in turn achieved by the removal or replacement of the object which is selected. In this paper, we propose a method with the aim of building upon the super-resolution based inpainting, which is based on examplarbased inpainting and single-image examplar which is also based on super resolution. The multiple inpainted combination versions of the input picture are the main aim of the proposed method. The motivation behind this technique is dealing with the sensitivity of examplar-based algorithms with respect to certain frameworks which includes filling order and patch size etc. When a new frame work is proposed which involves the combination of numerous inpainted description of the input image then the state of art examplar-based inpainting methods can be improved. Super resolution method is used in order to inpaint the input low resolution input image. Keywords— Inpainting, Image processing, Super resolution [SR], Texture.

I. INTRODUCTION With the modern developments in electronics, sensors and optics have put forwarded the widespread availability of video based surveillance and monitoring systems. In some of the imaging devices such as cameras, camcorders and surveillance cameras due to the factors such as quality of lenses, limited number of sensors in the camera etc makes a constrained achievable resolution. Increasing the quality of lenses or the number sensors will increased the cost of the device; in other cases the required resolution may not be still achievable with the current technology, however for better visualization, there will be many applications ranging from the security to broadcasting are providing the need for higher resolution images or videos [1]. In Image inpainting is related to filling in part of an image or video using information from the surrounding area. The goal is to produce a revised image in which the inpainted region is seamlessly merged into the image in a way that is not detectable by a typical viewer. The algorithm attempts to imitate basic approaches used by professional restorators. The algorithm also introduces the importance of propagating both the gradient direction (geometry) and gray-values (photometry) of the image in a band surrounding the hole to be filled-in. In the field of image processing, there exist many studies on image restoration and image enhancement such as image

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denoising [2], image deblurring [3], and image inpainting [4]. Furthermore, it is well known that the efficiency of these studies has been rapidly improved in recent years [5]. Missing area reconstruction is one of the most attractive concepts for study in the field of image restoration since it has a number of applications. Unnecessary object removal, missing block reconstruction in an error-prone environment in the wireless communication, and restoration of corrupted old films are representative applications. Since missing area reconstruction can be helpful in many applications, it has various names including inpainting, error concealment, image completion, and blotch and scratch removal. In its simplest form, the word image resolution is defined as the smallest discernible or measurable detail in a visual presentation [6]. it refers to the spacing of pixels in an image. The higher the special resolution the greater is the number of pixels in the image. In most image applications, images with a high resolution desired. This can be obtained by more sophisticated image acquisition hardware, since most digital imaging devices used charge coupled devices (CCD) and CMOS image sensors a higher resolution image can be obtained by reducing the pixel size by sensor manufacturing technique [7]. due to the decrease in pixel size, the amount of light available also decreases resulting in shot noise which degraded the image. Hence, the pixel size can be limited to a certain extent; current sensor technology has at present almost reached this level. Increasing chip size in order to accommodate larger number of pixels is also not viable due to an increase in capacitance with chip size and hence slower charge transfer rates. Some sort of post processing is needed which will enable in order to produce images of higher resolution than what the physical imaging system can produce. A high resolution (HR) image is obtained from one or more observed low resolution (LR) images. Super resolution (SR) uses Signal processing techniques due to which the problem of generating a high resolution image from one or more low resolution images [8] becomes easy. By estimating values on a finer grid and with the help of reconstruction methods increase in pixel resolution beyond the physical imaging systems is obtained. Restoration methods increase fidelity by correcting for acquisition artifacts such as blurring, aliasing and noise Images with greater resolution and higher fidelity than the lower resolution images are obtained by the combination of super resolution reconstruction and restoration. Three main tasks are included due to the super resolution process, an

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International Journal of Computer Trends and Technology (IJCTT) – Volume 14 Number 3 - Aug 2014 aliasing free up sampling of the image, which increases the maximum spatial frequency and removing degradations that arise during the image capture viz., blur and noise, in fact, the missing high frequency components are generated by the super resolution process, here high resolution images are generated which are related to the field of intense research and different methods which have been proposed. Super resolution image reconstruction techniques have proved useful in cases where greater clarity in images is required. Some of the applications such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) and surveillance systems with CCTV, remote and LANDSAT which are the imaging application of the satellite, and conversion of NTSC video signal to High Definition TV (HDTV) signals. These speckles, Scratches, and overlaid text are removed [9]–[10], [11], [12] by a number of algorithms which specially deal with the image stuffing issue for the effort of restoration. By propagating linear structures (called isophotes in the inpainting literature) by means of diffusion the holes in the images at the target region are filled by these image inpainting techniques. They are annoyed by the partial differential equations of physical heat flow and work persuasively as restoration algorithms. These diffusion processes introduces some blur, which is their drawback, which becomes noticeable while filling larger regions.

successful structure component reconstruction have traditionally been studied. Variational image inpainting technique is performed based on the continuity of the geometrical structure of images. Most variational inpainting techniques solve partial differential equations (PDEs). Although these variational image inpainting techniques enable successful reconstruction of the structure components, images also include other different important components, i.e., texture components, and alternative methods tend to output better results.

Figure 2: showing the inpainting before and after

Figure 3: process for improving the quality of image The various kinds of the inpainting methods used are A.

Figure 1: applications of inpainting before and after In this paper, we use ‘inpainting’ technique since this is one of the most common names in this research field. Inpainting methods are broadly classified into two categories: missing structure reconstruction [13] and missing texture reconstruction [14,15]. In addition, there have been proposed several inpainting techniques which adopt the combined use of the structure and texture reconstruction approaches [16]. Variational image inpainting techniques which aim at

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Diffusion based Inpainting

The first digital inpainting approach is Diffusion based Inpainting, here the information from the recognized portion is diffused into the missing portion of the pixel level. Generally variation method and Partial Differential Equation (PDE) are the basis for these methods, comparatively the diffusion based For filling the non textured or relatively smaller mission region good results are obtained basing on diffusion based algorithm but its only drawback is that it produces some blur, while filling the larger regions they are noticible on the other hand for completing small non textured target region PDE based inpainting is used. B.

Texture Synthesis base Inpainting the main objective of texture base inpainting which is assume to the initial methods of image inpaintng was to generate texture patterns, which are similar to the given sample pattern this is the way due to which the reproduced texture retains the statistical properties of the root texture

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International Journal of Computer Trends and Technology (IJCTT) – Volume 14 Number 3 - Aug 2014 [17].; the main theme of this algorithm was to use similar neighborhoods of the damaged pixels in order to complete the missing regions. Here the algorithm produces novel image pixels starting from the initial seed, where the local structure of the images are preserved [18], by sampling and copying pixels from the neighboring area was the process carried out by the earlier inpainting techniques. Whether the continuity is maintained between the existing pixels and inpainting holes creates the major difference.

based on single-image SR, which is again depends on sparse signal representation. Here the image patches are finely represented as a thin linear combination of elements from an suitably chosen from the complete dictionary. It is very difficult to Learn a broad class of image patches obtained from dictionary [22]. Dictionaries can be generated when raw patches are sampled from the training images which have same statistical nature [23]. G. Super Resolution through Neighbor Embedding

C.

PDE based Inpainting

PDE based inpainting algorithm is an example of iterative algorithm, in order to deal continuously with the geometric and photometric information that arrives at the border of the occluded area into area itself is the main idea behind this algorithm [19], for the smaller missed regions the results were comparatively good than that of the missing regions of large areas. Which can be overcome by using TV (total variation) which uses Euler Lagrange equation and anisotropic diffusion based on the strength of the isphotes? These algorithms are focused on the maintenance of the structure of the inpainted area so they produce a blur image which is the drawback of this method and also the large textured regions are not well reproduced, D. Exemplar based Inpainting. These inpainting algorithm belong to an important class and are very effective[20], they are designed to work in two steps, in the first priority assignment is done and in the second has the selection of the best matching patch. These method best samples the matching patches form the known region, where there similarity is measured by certain metrics, and which pastes the target patches the missing regions. Using spatial information of neighboring regions the missing regions are filled. E. Non-uniform Interpolation SR Technique The basis of this algorithm is obtained from the samples which are taken at non uniformly distributed location the reconstruction of the sampling theory is carried out. Detailed camera placements were allowed for accurate interpolation in earlier super-resolution applications, as this method is in need of very accurate registration between images. Taking comparatively low computational load and making real-time applications possible is the advantage of this approach [21]. These blur and the noise characteristics that are same for all LR images are applicable to the degradation models which are limited. F. Sparse Representation Method Researchers suggest that the dictionaries which are simply prepared can generate high-quality reconstructions, when they are together used with the sparse representation earlier, it is

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This technique is based on assumption that the manifolds are formed form the small patches in low and high resolution images with comparable local geometry in two dissimilar spaces, for solving single image super resolution problems [24] this method is used, when a low resolution image is given as an input, then a high resolution counterpart is generated using it, here the set of small overlapping image patches are represented by low- or high-resolution images. a feature vector is used for the representation of each patch which may either contrast, correlation, sum of entropy, entropy, variance of difference ,variance of sum, difference of entropy, homogeneity , change of luminance. H. Frequency Domain Method The connection between LR images and desired HR images are explained by certain system equations which are used by the relative motion between LR images was introduced by Tsai and Huang, the frequency domain approach makes a vital role in using aliasing that exist in each LR image to reconstruct an HR image [11], it is based on three principles [25].These properties are helpful in order to aim the system equation related to the aliased DTF coefficients. I.

Projection onto Convex Sets

A linear replica which describes the relative of HR and LR images is the basis of this method; HR image is obtained by the cost function. Simplicity is one of the major advantages of this algorithm, for any smooth movement occasion it can be applied, prior information can be easily joined, and here the relaxation operator is used which improves the stability and presentation of the algorithm at the same time it does not contribute to the continuation of the image details. There are certain disadvantage such as non uniqueness of solution, slow convergence and high computational cost. II. RELATED WORK Texture synthesis based methods are one of the initial methods of image Inpainting. With the help analogous neighbourhoods of the damaged pixels these methods were very helpful in completing the missing regions of the image to be inpainted.. Texture patterns are generated from texture synthesis based inpainting method, which resembles to a given sample pattern, in a way that the reproduced texture

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International Journal of Computer Trends and Technology (IJCTT) – Volume 14 Number 3 - Aug 2014 resembles the statistical properties of the root texture. [26]. PDE based Inpainting method is the iterative algorithm. in order to deal continuously with the geometric and photometric information that arrives at the border of the occluded area into area itself is the main idea behind this algorithm [27]. This is done by propagating the information in the direction of the minimal change using isophote lines. This method will produce good results if the missed regions are small one. But when the missed regions are very large this algorithm will take so long time and it will not produce good results. The exemplar based approach is an important class of inpainting methods and they have proved to be very effective. Basically it consists of two basic phases: in the first phase priority assignment is done and the second phase consists of the selection of the best matching patch. The exemplar based method samples the best matching patches from the known region, whose similarity is measured by certain metrics, and then pastes into the target patches in the missing region The samples which are collected from non-uniformly distributed locations are considered as the base for the non uniform interpolation of super-resolution methods, whose functions are reconstructed with the help of the non uniform sampling theory. In order to allow accurate interpolation the detailed camera placements were used by the early super-resolution applications, as accurate – registration between the images were required by this technique.Taking relatively low computational load and makes real-time applications possible is the advantage of this method [28]. The single-image super-resolution problems are solved with the help of the techniques used from SR through neighbor embedding [29]. High-resolution counterpart is recovered from the given low resolution image as the input using a group of training examples. Only non negative values were considered with the neighbor embedding method based on (SNMF). In this method, the weights are controlled to sum up to one, but no constraints are precise for their sign. This may explain the unstable results, Subtractive combination of patches which are counterintuitive are obtained which are directed to the possible negative values the aliasing that exists in each low resolution image to reconstruct an HR image is utilized effectively by the frequency domain approach. III. PROPOSED WORK Two main sequential operations are composed in the proposed work, filling of missing regions are done by the non parametric patch sampling technique, the inpainted method is applied on the required region of the input image rather than filling the missing regions with the original resolution. Performing the inpainting process on a low-resolution image has many reasons. First, the coarse version of the input image could be compared to a gist representing dominant and important structures. Secondly, when compared to the original image the image to be inpainted is smaller, the time taken to inpaint the required portion of the input original image is less

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when compared to the full resolution picture. To increase the resolution and quality of the inpainted image are its goals. As a result of the first phase of inpainting a low resolution image is obtained, with the help of couple of training example which are collected from the know parts of the input image, then it is converted to high resolution here the single image SR image approximation is used.Figure 2 the main idea behind the proposed work is described here. The main mechanisms are super resolution algorithm and the required inpainting techniques.

Figure 2: The framework of the proposed method 1.

Examplar-based Inpainting

This part describes the inpainting technique which is used to fill in the low resolution images. The proposed examplarbased technique follows the two steps- the texture synthesis and patch priority filling order. The structures form the texture are distinguished by considering the measure of the priority for each patch which is defined by the filling order computation .Three different data terms have been tested, sparsity-based priority and tensor-based priority and gradientbased priority.Figure 3 Describes the Inpainting of low resolution pictures with dissimilar gradient-based priority (first row), tensor-based priority (second row) and sparsitybased priority (third row).

Figure 3: Results of different inpainting techniques

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International Journal of Computer Trends and Technology (IJCTT) – Volume 14 Number 3 - Aug 2014 In sparsity-based priority, a template matching for an identical part of the image, let  pi be a current patch and  Pj be an adjoining patch between which a search window is performed where w p,pj is considered as the weight which is calculated for every pair of the patches.With the help of a non-local means approach,. The sparsity term is defined as follows:

Where Ns represent the number of valid patches and N represents the whole number of the candidates who are present in the search window. In texture synthesis, the patch with high priority is filled first. In order to fill the unknown parts of the current patch two sets of the candidates are used. Where the first set is composed of K majority same patches which are located in the neighbourhood of the local.centered on the current patch. They are combined with the help of a non-local means approach. The weighting factors are defined as follows:

Figure 4: The low-resolution picture is inpainted with different settings Sx (Table -1) Table-1 13 Configurations for filling the unknown parts of the pictures Setting 1 (default)

2 3 4 Where d() is a metric indicating the similarity between patches, and “h” is the decay factor. 2.

5 6

Combining Multiple Inpainted Images

A final inpainted picture is generated from the M inpainted pictures which is the major focus of multiple inpainted images. Figure 3 illustrates some inpainted results obtained for a given setting. To obtain the final inpainted image, three kinds of sequences are considered, where the intial two techniques are simple because each pixel value in the final picture is achieved by either the average or the median operator as given below:

7 8

9 10 11

12 13

3.

Parameters Patch’s size 5 × 5 Decimation factor n = 3 Search window 80 × 80 Sparsity-based filling order default + rotation by 180 degrees default + patch’s size 7 × 7 default + rotation by 180 degrees + patch’s size 7 × 7 default + patch’s size 11 × 11 default + rotation by 180 degrees + patch’s size 11 × 11 default + patch’s size 9 × 9 default + rotation by 180 degrees + patch’s size 9 × 9 default + patch’s size 9 × 9 + Tensor-based filling order default + patch’s size 7 × 7 + Tensor-based filling order default + patch’s size 5 × 5 + Tensor-based filling order default + patch’s size 11 × 11 + Tensor-based filling order default + rotation by 180 degrees + patch’s size 9 × 9 + Tensorbased filling order

Super Resolution Algorithm

On completion of multiple low resolution inpainted pictures, in order to reconstruct the details of the picture a

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International Journal of Computer Trends and Technology (IJCTT) – Volume 14 Number 3 - Aug 2014 hierarchical single image SR comes,. The steps of the algorithm are as follows (Figure 4):

The priority values are propagated when the current patch are filled, Steps are described on the existence of unknown areas

 Dictionary building: it simply consists of the image patches which are formed the corresponding high and low resolution images. The unique limitation is that they only consist of pixels which are known.  Filling order of the HR picture: The calculation of the filling order based on high resolution picture with the sparsity-based method. The filling process starts with the HR

patch  px which has the highest priority was calm of famous and unfamiliar areas.  The low resolution patch has the highest priority when compared with that of the high resolution patch, the best neighbour of the low resolution is found in the available inpainted images, along with the dictionary and surrounding of the local inpainted image this search is performed.

Figure 6: Super resolution Algorithm In figure 6, the red block indicates missing areas which are filled in by using the best candidate stemming also from the dictionary. The peak image indicates the unique image with the missing regions while the base one is obtained from LR in painting.

Figure 5: it discribes how a super resolution image is extracted from a normal low resolution image by storing there information in the dictionary The process involved in figure 5 is described in detail as first a dictionary full of facial images is created for super resolution; these collected images are shrunk to deliberately create rough images, similar patterns of both images are then extracted and registered in the dictionary as a set other parts are similarly registered and the dictionary is complete , when a low resolution facial image is entered, one facial area is extracted, a similar facial pattern is searched from the dictionary, when it is found the high resolution version is exported. The nose is similarly processed. The same image processing for the entire face recreates a super-resolution image. This is how using this technology one can accurately reproduce a facial image.

IV. CONCLUSION Inpainting is the practice where lost images are reconstructed with the help of the information present in the background of the undisturbed part of the image. These missing regions are reconstructed from the spatial information which is available from the neighboring regions, these methods of inpainting exhibits a number of applications, these applications are also useful for the reconstruction of old films and unwanted object removal in the digital photographs.. High resolution images from sequence of low resolution images are generated from SR reconstruction procedure. Improving the visual quality of available low resolution image is the main objective of super resolution technique. With help of the super resolution reconstruction the existing Low Resolution imaging can be utilized. SR related inpainting techniques based inpainting technique is performed first on more damaged part of the main input image. The missing area details are collected with the help of this method and it has the advantage for easy inpainting of low resolution pictures when compared to that of the high resolution pictures. Future works: lots of brand new technologies basing on this super resolution will be available in the near future, when a

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International Journal of Computer Trends and Technology (IJCTT) – Volume 14 Number 3 - Aug 2014 fugitive’s surveillance image is released on the news, it is usually too grainy to make out the face by using this technology, facial features can be re produced in high resolution, it can be combined with facial recognition technology, which would make individual identification possible form an image of a crown in a street or stadium or in any public meeting, it can also be utilized in the business and security applications also. REFERENCES [1] M. Ao, D. Yi, Z. Lei, and S. Z. Li. Handbook of remote biometrics, chapter Face Recognition at a Distance: System Issues, pages 155–167. Springer London, 2009. [2] A Buades, B Coll, J Morel, A review of image denoising algorithms, with a new one. Multiscale Model. Simul. 4(2), 490–530,2005. [3] L Shao, H Zhang, G de Haan, An overview and performance evaluation of classification-based least squares trained filters . IEEE Trans. Image Process. 17(10), 1772–1782, 2008. [4] Z Tauber, ZN Li, M Drew, Review and preview: disocclusion by inpainting for image-based rendering. IEEE Trans. Syst., Man, Cybern., Part, C: Appl. Rev. 37(4), 527–540, 2007. [5] L Shao, R Yan, X Li, Y Liu, From heuristic optimization to dictionary learning: A review and comprehensive comparison of image denoising algorithms. IEEE Trans. Cybern, 2013. [6] Subhasis Chaudari, Super Resolution Imaging. Kluwer Academic Publishers, norwell, MA, USA, 2001. [7] Sung Cheol Park, Min Kyu Park, and Moon ki Kang. Super Resolution image reconstruction: A technical overvieow. IEEE Signal processing Magazine, 20(3):21-36, May 2003. [8] Hong Chang, Dit-Yan Yeung and Yimini Xiong. Super resolution through neighbour embedding CVPR, 01:275-282,2004 [9] C. Ballester, V. Caselles, J. Verdera, M. Bertalmio, and G. Sapiro, “A variational model for filling-in gray level and color images,” in Proc. Int.Conf. Computer Vision, Vancouver, BC, Canada, June 2001, pp. 10–16.

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