Analysis of Image Inpainting Techniques with Exemplar, Poisson, Successive Elimination and 8 Pixel Neighborhood Methods

Abstract

This paper discusses removing large objects from digital images and fills the hole that is left behind in a visually plausible way. We present a novel and efficient algorithm that fills the hole by exemplar-based synthesis. Here the simultaneous propagation of texture and structure information is achieved by a single, efficient algorithm. The texture image is repaired by the exemplar -based method; for the structure image, the Laplacian operator is employed to enhance the structure information, and the Laplacian image is inpainted by the exemplar-based algorithm, followed by a reconstruction based on the Poisson equation. To improve the computational efficiency of our algorithm we go for successive elimination algorithm (SEA). In 8 pixel neighborhood method, identifying central pixel value by investigating surrounded 8 neighborhood pixel properties like color variation, repetition, intensity and direction. Finally we compare speed and accuracy of a picture enhancement using 8 pixel neighborhood with exemplar based poisson & successive elimination method

Three Dimensional Reconstruction of Human Heart Surface From Single Image- View Under Different Illumination Conditions

“…From the results reported in Table 2, it can be seen that the accuracy of the proposed method has improved on the other methods presented in (Arnold et al, 2010;Muthukumar et al, 2010). Moreover, the cost reduction of the False Positives (FP) and False Negatives (FN) for the proposed method compared to (Arnold et al, 2010) and (Muthukumar et al, 2010) was 76.26% and 76.16%, respectively.…”

“…Moreover, the cost reduction of the False Positives (FP) and False Negatives (FN) for the proposed method compared to (Arnold et al, 2010) and (Muthukumar et al, 2010) was 76.26% and 76.16%, respectively. Furthermore, the proposed method can process the image faster due to the need for only four neighbouring pixels in the correction process.…”

“…The results are visually shown in Fig. 12, which compares the results of the three methods in the form of the original image, the results from the proposed Г method, the results from the inpainting method (Arnold et al, 2010) and the results from the eight neighbour pixel method (Muthukumar et al, 2010), respectively. It can be seen that the results of the proposed method are the best compared to the other methods, while the inpainting method has a better result in the dark regions.…”

“…Table 2. Proposed method Compared to the method in (Arnold et al, 2010) and to the eight neighbour pixels method in (Muthukumar et al, 2010). The proposed method achieved a cost reduction 76.16% compared to the method in (Muthukumar et al, 2010 …”

“…The quantitative test results from these data sets are given in Table 1 The evaluation Table 1, started by manually creating a set of ground truths by labelling and correcting the specular pixels in a set of images of a human heart from different patients. Then the performance of the proposed method was compared with the inpainting algorithm proposed by (Arnold et al, 2010) and the eight neighbour pixels method by (Muthukumar et al, 2010). For each image, we optimized the parameters of the (Arnold et al, 2010) method and the eight neighbour pixels method (Muthukumar et al, 2010), while the proposed method was automatically optimized.…”