scholarly journals Image Inpaint Using Patch Sparsity

2020 ◽  
Vol 1 (3) ◽  
pp. 85-91
Author(s):  
Shital D. Suryawanshi ◽  
P. V. Baviskar
Keyword(s):  
Digital Image ◽  
Efficient Algorithm ◽  
Image Inpainting ◽  
Background Information ◽  
Structure Information ◽  
Left Behind ◽  
Specific Object ◽  
Actual Color

The process of removing the specific object or area or repairing the damaged area in an image is known as image inpainting. This algorithm [5] is proposed for removing objects from digital image. The challenge is to fill in the hole that is left behind in a visually plausible way. We first note that patch sparsity based synthesis contains the essential process required to replicate both texture and structure [8]; the success of structure propagation however is highly dependent on the order in which the filling proceeds. We propose a best algorithm in which the confidence in the synthesized pixel values is propagated in a manner similar to the propagation of information in inpainting.The actual color values are computed using patch sparsity based synthesis. In this paper the simultaneous propagation of texture and structure information [2] is achieved by a single, efficient algorithm. For best results selected image should have sufficient background information

scholarly journals Image Inpaint Using Patch Sparsity

2016 ◽  
Vol 1 (3) ◽  
pp. 85-91
Author(s):  
Shital D. Suryawanshi ◽  
P. V. Baviskar
Keyword(s):  
Digital Image ◽  
Efficient Algorithm ◽  
Image Inpainting ◽  
Background Information ◽  
Structure Information ◽  
Left Behind ◽  
Specific Object ◽  
Actual Color

The process of removing the specific object or area or repairing the damaged area in an image is known as image inpainting. This algorithm [5] is proposed for removing objects from digital image. The challenge is to fill in the hole that is left behind in a visually plausible way. We first note that patch sparsity based synthesis contains the essential process required to replicate both texture and structure [8]; the success of structure propagation however is highly dependent on the order in which the filling proceeds. We propose a best algorithm in which the confidence in the synthesized pixel values is propagated in a manner similar to the propagation of information in inpainting.The actual color values are computed using patch sparsity based synthesis. In this paper the simultaneous propagation of texture and structure information [2] is achieved by a single, efficient algorithm. For best results selected image should have sufficient background information.

scholarly journals Direction Oriented Block based Inpainting using Morphological Operations

2019 ◽  
Vol 8 (12) ◽  
pp. 2769-2774
Keyword(s):  
Image Processing ◽  
Basic Concept ◽  
Efficient Algorithm ◽  
Image Inpainting ◽  
Experimental Results ◽  
Background Information ◽  
Morphological Operations ◽  
Original Image ◽  
Computationally Efficient ◽  
Block Based

Inpainting is one of the wide growing area of image processing. Image inpainting is a technique to reconstruct the restored region using some background information and obtain the results very efficiently and effectively. The basic concept of image inpainting is to replace the unwanted object from the original image and to recover the image using some neighborhood pixels in an undetectable way. In this paper, we introduce an efficient algorithm for image inpainting i.e., Direction Oriented Block-Based using Morphological Operations approach. This algorithm gives better efficiency than the previously proposed algorithm. By this approach, we can inpaint large regions as well as recover small portions in an undetectable way. A more accurate patch will be found out by this proposed approach. The experimental results proved that our proposed work is more computationally efficient and effective compared to previous work.

scholarly journals An efficient algorithm for determining positions of astronomical objects in the Deep Sky Object pictures

2015 ◽  
Vol 63 (3) ◽  
pp. 679-684
Author(s):  
R. Suszynski ◽  
K. Wawryn ◽  
M. Dziebowski
Keyword(s):  
Image Processing ◽  
Atmospheric Turbulence ◽  
Digital Image Processing ◽  
Digital Image ◽  
Efficient Algorithm ◽  
Automatic Identification ◽  
Matlab Program

Abstract The article presents an algorithm for digital image processing of astronomical objects in order to effectively determine the position of these objects. The proposed method has been optimized due to its effectiveness of removing noise and distortion caused by atmospheric turbulence and imperfections in long exposure photography of astronomical objects. This solution is ready for implementation in a system for automatic identification of stars in the recorded images. Such a system is designed for GoTo circuits at telescope’s drives, which can automatically point a telescope to astronomical objects. The method was verified by simulation in MATLAB program on real images of astronomical objects.

scholarly journals Pose Estimation of Uncooperative Unknown Space Objects from a Single Image

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Xiaoyuan Ren ◽  
Libing Jiang ◽  
Zhuang Wang
Keyword(s):  
Pose Estimation ◽  
Experimental Results ◽  
Single Image ◽  
Space Object ◽  
Shape Model ◽  
Structure Information ◽  
Space Objects ◽  
Specific Object ◽  
Unknown Objects ◽  
Unknown Object

Estimating the 3D pose of the space object from a single image is an important but challenging work. Most of the existing methods estimate the 3D pose of known space objects and assume that the detailed geometry of a specific object is known. These methods are not available for unknown objects without the known geometry of the object. In contrast to previous works, this paper devotes to estimate the 3D pose of the unknown space object from a single image. Our method estimates not only the pose but also the shape of the unknown object from a single image. In this paper, a hierarchical shape model is proposed to represent the prior structure information of typical space objects. On this basis, the parameters of the pose and shape are estimated simultaneously for unknown space objects. Experimental results demonstrate the effectiveness of our method to estimate the 3D pose and infer the geometry of unknown typical space objects from a single image. Moreover, experimental results show the advantage of our approach over the methods relying on the known geometry of the object.

Fractal Digital Image Inpainting

2010 ◽  
Vol 4 (4) ◽  
pp. 140-150 ◽  
Author(s):  
LI Jinjiang ◽  
FAN Hui ◽  
YUAN Da
Keyword(s):  
Digital Image ◽  
Image Inpainting

Optical Camouflage Effect Assessment Based on Digital Image Inpainting Technology

2018 ◽  
Vol 55 (3) ◽  
pp. 031011
Author(s):  
王鹏烨 Wang Pengye ◽  
赵德辉 Zhao Dehui ◽  
李明锋 Li Mingfeng
Keyword(s):  
Digital Image ◽  
Image Inpainting ◽  
Effect Assessment

Adaptive digital image inpainting

2004 ◽  
Author(s):  
T.K. Shih ◽  
Rong-Chi Chang ◽  
Liang-Chen Lu ◽  
Wen-Chieh Ko ◽  
Chun-Chia Wang
Keyword(s):  
Digital Image ◽  
Image Inpainting

Study on Digital Image Inpainting Method Based on Multispectral Image Decomposition Synthesis

2018 ◽  
Vol 33 (01) ◽  
pp. 1954004 ◽  
Author(s):  
Zhaoyang Jia ◽  
Guangxue Chen
Keyword(s):  
Digital Image ◽  
Polynomial Regression ◽  
Image Inpainting ◽  
Principal Component ◽  
Image Decomposition ◽  
Multispectral Image ◽  
High Fidelity ◽  
Multispectral Images ◽  
Image Appearance ◽  
Digital Inpainting

The paper analyzes the image inpainting problem of damaged Painting Arts for high fidelity images reproduction, and a digital image inpainting method based on multispectral image decomposition synthesis is proposed. Firstly, multi-channel images of Painting Arts are obtained by multispectral technology. Then, a polynomial regression method based on principal component is used to reconstruct the spectral image. The reconstructed image is decomposed by VO image decomposition model. During the inpainting process, the channel correlation of the structure image and the texture image of multispectral image is effectively removed. The digital image inpainting is performed respectively. Finally, the digital inpainted image is obtained by synthesis. The experimental results show that the digital image inpainting based on multispectral image decomposition synthesis reduces the problem of low image inpainting accuracy caused by the correlation between the color components in the traditional digital image inpainting process, and reduces the mismatch of the inpainting image. Appearance of pseudo color of inpainting image is reduced. MSE of multispectral images inpainting qualities is 2.7951 and PSNR of multispectral images inpainting qualities is 44.1681, so it is superior to traditional image inpainting algorithm. It provides a reliable basis for digital inpainting, digital archives and high fidelity replication of defective Painting Arts.

scholarly journals MUSICAL: Multi-Scale Image Contextual Attention Learning for Inpainting

2019 ◽  
Author(s):  
Ning Wang ◽  
Jingyuan Li ◽  
Lefei Zhang ◽  
Bo Du
Keyword(s):  
Image Inpainting ◽  
Superior Performance ◽  
Background Information ◽  
Feature Maps ◽  
Background Region ◽  
Multi Scale ◽  
The Arts ◽  
Deep Feature ◽  
Style Consistency ◽  
Similar Texture

We study the task of image inpainting, where an image with missing region is recovered with plausible context. Recent approaches based on deep neural networks have exhibited potential for producing elegant detail and are able to take advantage of background information, which gives texture information about missing region in the image. These methods often perform pixel/patch level replacement on the deep feature maps of missing region and therefore enable the generated content to have similar texture as background region. However, this kind of replacement is a local strategy and often performs poorly when the background information is misleading. To this end, in this study, we propose to use a multi-scale image contextual attention learning (MUSICAL) strategy that helps to flexibly handle richer background information while avoid to misuse of it. However, such strategy may not promising in generating context of reasonable style. To address this issue, both of the style loss and the perceptual loss are introduced into the proposed method to achieve the style consistency of the generated image. Furthermore, we have also noticed that replacing some of the down sampling layers in the baseline network with the stride 1 dilated convolution layers is beneficial for producing sharper and fine-detailed results. Experiments on the Paris Street View, Places, and CelebA datasets indicate the superior performance of our approach compares to the state-of-the-arts. 

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