An image inpainting method using pLSA-based search space estimation

Ghorai, Mrinmoy; Chanda, Bhabatosh

doi:10.1007/s00138-014-0647-9

Cited by 15 publications

(5 citation statements)

References 36 publications

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“…In all of the Cases, it can be observed that certain damaged regions of the image, especially the structural information, could not be restored by the previous SOM method due to the fact that the algorithm is more suitable for scratch removal. The results in all of the Cases demonstrate the ability of our proposed method to inpaint both texture and structure better than the other methods proposed in [13], [18] and [17].…”

Section: A Subjective Visual Evaluationmentioning

confidence: 74%

“…From Table 1, we can see that our proposed method gets higher PSNR values than the methods proposed in [13], [17] and [18]. Furthermore, the mean PSNR of 29.2479 by the proposed method is much higher than that of the other three approaches.…”

Section: B Quantitative Evaluationmentioning

confidence: 84%

“…For all the eight Cases, we compare our proposed method with other state of the art methods such as the TV method and the previous SOM based method. In all the figures of the different Cases studied below, (a) is the damaged picture, (b) is the image resulting from using the TV method in [13], (c) is the image resulting from using the previous SOM based method in [18], (d) is the image resulting from using the exemplarbased method in [17], and (e) is the image resulting from using the novel method proposed in this paper. It can be seen that (e) is always the best result.…”

Section: Resultsmentioning

confidence: 99%

“…The other widely used inpainting technique is the exemplar-based method [16]. They either work at the pixel level or at the patch level [17]. These techniques are good for textured and large missing regions, but they may sometimes produce visually inconsistent results.…”

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Structure-Priority Image Restoration Through Genetic Algorithm Optimization

et al. 2020

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With the significant increase in the use of image information, image restoration has been gaining much attention by researchers. Restoring the structural information as well as the textural information of a damaged image to produce visually plausible restorations is a challenging task. Genetic algorithm (GA) and its variants have been applied in many fields due to their global optimization capabilities. However, the applications of GA to the image restoration domain still remain an emerging discipline. It is still challenging and difficult to restore a damaged image by leveraging GA optimization. To address this problem, this paper proposes a novel GA-based image restoration method that can successfully restore a damaged image. We name it structure-priority image restoration through GA optimization. The main idea is to convert an image restoration task into an optimization problem, and to develop a GA optimization algorithm to solve it. In this study, the structural information of a damaged image, which is represented by curves or lines (COLs), is prioritized to be repaired first. The structural information is classified into relevant and irrelevant information according to the information of their locations. The relevant information is analyzed through the proposed GA optimization algorithm to find the matched COLs. The matched COLs are used to restore the structural information of the damaged area. The textural information will then be restored according to the different partitions separated by the restored structural information. Lastly, through case studies, we evaluate the proposed method by using four typical indices to measure the differences between the original and restored image. The results of case studies demonstrate the applicability and feasibility of the proposed method. INDEX TERMS Genetic algorithm, image processing, image restoration, relevant information, structurepriority, textural information, curves or lines (COLs). I. INTRODUCTION With the advent of social networking platforms, more and more people are sharing their thoughts about products or services by posting text messages, images, audio or video files [1]. Vision is the most advanced human sense [2], as a result, as social media data accumulates, it is not surprising that images play an important role and have become a more and more popular form of data on the Internet. With the significant increase in the use of images over the Internet, image processing and analysis have been gaining much research attention in the transportation, medical, aerospace, energy and other fields [3]-[6]. The associate editor coordinating the review of this manuscript and approving it for publication was Kathiravan Srinivasan .

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Section: A Subjective Visual Evaluationmentioning

confidence: 74%

Section: B Quantitative Evaluationmentioning

confidence: 84%

Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

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Structure-Priority Image Restoration Through Genetic Algorithm Optimization

et al. 2020

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“…Jin [10] represented the image features as dictionaries through the BOW model, and used the Probabilistic Latent Semantic analysis (PLSA) model to find out the potential themes from a large number of images to classify the images. Ghorai [11] assumed that the potential topic space could be learned from the two modes of vision and text. PLSA was used to learn the data in different models to obtain the corresponding semantic topic distribution, and then the two models were fused through adaptive asymmetric algorithm to obtain better classification effect.…”

Section: Introductionmentioning

confidence: 99%

Modified generative adversarial networks for image classification

Zhao

2021

Evol. Intel.

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In the image classification task, the existing neural network models have insufficient ability to characterize the features of the classified objects, which leads to the problem of low recognition accuracy. Therefore, we propose a modified Generative Adversarial Networks (GAN) for image classification. Based on the traditional generative adversarial network, By constructing multiple generation models and introducing collaboration mechanism, the generation models can learn from each other and make progress together in the training process to improve the fitting ability of the model for real data and further improve the classification quality. Finally, a generative adversarial network is designed to generate the occlusion samples, so that the model has good robustness for the occlusion objects recognition. The Top-1 error rate is used as the evaluation index. The experiments are conducted on the public data sets containing Cifar10, Cifar100, ImageNet2012. The comparison experiment results show that the proposed method can improve the feature representation ability of the GAN and improve the accuracy of image classification. The average accuracy is higher than 90% and the error rate is lower than 1.0%.

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Digital Restoration for Damaged Thangka Image

Zen

Meng

et al. 2019

Application of Intelligent Systems in Multi-Modal Information Analytics

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An image inpainting method using pLSA-based search space estimation

Cited by 15 publications

References 36 publications

Structure-Priority Image Restoration Through Genetic Algorithm Optimization

Structure-Priority Image Restoration Through Genetic Algorithm Optimization

Modified generative adversarial networks for image classification

Digital Restoration for Damaged Thangka Image

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