Exemplar Inpainting Research Articles

A Hybrid Inpainting Model Combining Diffusion and Enhanced Exemplar Methods

Image inpainting is a technique in the world of image editing where missing portions of the image are estimated and filled with the help of available or external information. In the proposed model, a novel hybrid inpainting algorithm is implemented, which adds the benefits of a diffusion-based inpainting method to an enhanced exemplar algorithm. The structure part of the image is dealt with a diffusion-based method, followed by applying an adaptive patch size–based exemplar inpainting. Due to its hybrid nature, the proposed model exceeds the quality of output obtained by applying conventional methods individually. A new term, coefficient of smoothness, is introduced in the model, which is used in the computation of adaptive patch size for the enhanced exemplar method. An automatic mask generation module relieves the user from the burden of creating additional mask input. Quantitative and qualitative evaluation is performed on images from various datasets. The results provide a testimonial to the fact that the proposed model is faster in the case of smooth images. Moreover, the proposed model provides good quality results while inpainting natural images with both texture and structure regions.

Improved Exemplar based Image Inpainting for Partial Instance Occlusion Handling with K-means Clustering and YCbCr Color Space

The images acquired in real time outdoor environment are often subject to uneven illumination conditions, cloudy weather, lighting conditions. The instances of partial occlusions deteriorate the background modeling of such scenes. Varying illumination outdoor scene set of images with partial occlusions are addressed through this investigative work. There is a need for a restoration method that finds improved subjective perception and execution time. The proposed work focuses on novel amended exemplar model to improve the subjective perception. The exemplar inpainting method is improved through the color quantization with K-means clustering approach in YCbCr color space. Experimental validations and proposed method results show better improvement in qualitative and objective measures than existing methods. The average “Peak Signal to Noise Ratio (PSNR)” as 28.2869 and “Structural SIMilarity index (SSIM)” as 0.9759 of the proposed method has shown better results respectively both visually and with a tradeoff in time.

Coherent Spatial and Colour Blended Exemplar Inpainting

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.

Latent Fingerprint Recognition using Hybridization Approach of Partial Differential Equation and Exemplar Inpainting

Objectives: Biometric based Fingerprint recognition system is one of the well-adapted approaches in online security prospects. However, due to user-friendly behaviour of advanced computing systems, biometric approach is not much in use now days. The approach left its footprints only with the applications to identify criminal activities at crime scenes where fingerprints are mainly available in latent form. Latent fingerprints are the accidently left finger skin impressions by criminals. These impressions are invisible for the naked human eye and usually captured with lasers, chemical, powders etc. These captured latent fingerprints carries less minutiae information with distorted ridges and high level of pattern overlapping. So, it is not easy to identify the criminals with partial fingerprint information. Methods/Statistical Analysis: In this paper, hybrid approach of Exemplar In painting and Partial Differential Equation is used to fill up the distorted ridges. The main goal of this work is to present the framework to reconstruct the latent distorted fingerprint and further use them to find the best match for those enhanced reconstructed latent fingerprints. For the experimentation of proposed hybrid concept, IIIT Delhi latent and NIST SD-27 databases of fingerprint are used. In this experimentation, different fingerprint enhancement filters like Canny Edge Detection Filter, Prewitt filter, Laplacian Filter, Sobel Filter, Gaussian Low Pass Filter, Gaussian High Pass Filter are used. Findings: Different filters show different performance of dataset images for latent fingerprint recognition. The overall performance of the automated latent fingerprint identification approach is analysed in terms of false acceptance rate and genuine acceptance rate. Application/Improvements: In this way, latent fingerprint can be used for the recognition of criminal activities at crime scenes where fingerprints are mainly available in latent form. The overall concept shows better results for canny filter as compare to other considered filters in enhancement.

Image Restoration using Prioritized Exemplar Inpainting with Automatic Patch Optimization

Image analysis and restoration has been carried out by texture synthesis for large regions and inpainting algorithms for small cracks in images. A new approach that allow simultaneous fill-in of different structures and textures is being proposed in present study. A combination of structure inpainting and patch-based texture synthesis with patch size optimization is carried out (termed as ‘optimized patch based inpainting’) for filling and updating the target region that shows additional advantages over earlier approaches. The algorithm here uses the patch based inpainting having isophote driven patch-based texture synthesis at core. In present algorithm, ones the user selects the target regions, the algorithm automatically search and fills-in these regions with best matching information surrounding it. Patch-based filling is found to improve execution speed and robustness significantly over pixel-based fill. The fill-in is done in such a way that structure information arriving at the region boundaries is propagated inside. A number of examples on real and complex images are considered to demonstrate the effectiveness of the algorithm.

Modified Exemplar based Image Inpainting Algorithm

Recovering lost part of an image plays a great role in image processing. Inpainting is a technique that helps in recovering lost pixels from an image. From the existing techniques of Inpainting, Exemplar Inpainting is one of the fast and better techniques that help in restoring the lost part of an image. Exemplar based method chooses a patch similar to the lost patch from the known area to fill in the occluded surface. This paper proposes a modified exemplar based Inpainting algorithm for restoring the unknown pixels in a lost region in an image. In the proposed work, the pixel at the contour of the missing region is prioritized by taking into consideration the most known pixels around. In order to find the strength of a pixel in this proposed approach, a Sobel detector is used and the magnitude of the pixel is found. Further the most similar patch to the unknown region is searched in the known area using structural similarity index. The experimental result shows that this method shows an improvement in filling the lost region by finding the exact match from the known area of the given image.

MRF exemplar inpainting algorithm based on dual-tree complex wavelet domain

To eliminate the mosaic and "bell" effects due to cumulative errors during large object image inpainting,the Markov Random Fields(MRF) exemplar inpainting based on dual-tree complex wavelet domain was proposed.The image was converted to complex-frequency domain by Dual-Tree Complex Wavelet Transform(DTCWT) and the exemplar inpainting order was computed by rational confidence and data item,the unknown region was inpainted based on multiscale and multiband.The inpainted images were reconstructed by dual-tree complex wavelet inverse transform.The experimental results show that compared with classical discrete wavelet methods,the mosaic and "bell" effects can be avoided and the more favorable textural and structural information can be preserved.