PEPSI++: Fast and Lightweight Network for Image Inpainting

Shin, Yong-Goo; Sagong, Min-Cheol; Yeo, Yoon-Jae; Kim, Seung-Wook; Ko, Sung-Jea

doi:10.1109/tnnls.2020.2978501

Cited by 88 publications

(66 citation statements)

References 22 publications

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“…But here we choose to present just PSNR and SSSIM of the image inpainting results on the images where the block is located in the center. In [45], two datasets are used with two types of distortion including blocks and free-form masks, which are categories of scratch painted with bold lines. For this example, we can see that the inpainting of scratch is more accurate that repairing the blocks .…”

Section: Evaluation and Discussionmentioning

confidence: 99%

“…At present, Generative Adversarial Network (GAN) becomes the most used technique in all computer vision applications. GAN-based approaches use a coarse-to-fine network and contextual attention module gives good performance and is proven to be helpful for inpainting [43][44][45][46][47]. Existing image inpainting methods based on GAN are generally a few.…”

Section: Gan-based Approachesmentioning

confidence: 99%

“…The methods use improved GoogLeNet and deep convolutional generative adversarial network (DCGAN). In a research work named PEPSI++ [45], Shin et al propose a GAN-based method for image inpainting. Wang et al [46] use Encoder-decoder network and multi-scale GAN for image inpainting.…”

Section: Gan-based Approachesmentioning

confidence: 99%

See 2 more Smart Citations

Image Inpainting: A Review

Elharrouss

Almaadeed

Al‐Maadeed

et al. 2019

Neural Process Lett

245

100

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Although image inpainting, or the art of repairing the old and deteriorated images, has been around for many years, it has gained even more popularity because of the recent development in image processing techniques. With the improvement of image processing tools and the flexibility of digital image editing, automatic image inpainting has found important applications in computer vision and has also become an important and challenging topic of research in image processing. This paper is a brief review of the existing image inpainting approaches we first present a global vision on the existing methods for image inpainting. We attempt to collect most of the existing approaches and classify them into three categories, namely, sequential-based, CNN-based and GAN-based methods. In addition, for each category, a list of methods for the different types of distortion on the images is presented. Furthermore, collect a list of the available datasets and discuss these in our paper. This is a contribution for digital image inpainting researchers trying to look for the available datasets because there is a lack of datasets available for image inpainting. As the final step in this overview, we present the results of real evaluations of the three categories of image inpainting methods performed on the datasets used, for the different types of image distortion. In the end, we also present the evaluations metrics and discuss the performance of these methods in terms of these metrics. This overview can be used as a reference for image inpainting researchers, and it can also facilitate the comparison of the methods as well as the datasets used. The main contribution of this paper is the presentation of the three categories of image inpainting methods along with a list of available datasets that the researchers can use to evaluate their proposed methodology against.

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Section: Evaluation and Discussionmentioning

confidence: 99%

Section: Gan-based Approachesmentioning

confidence: 99%

See 1 more Smart Citation

Image Inpainting: A Review

Elharrouss

Almaadeed

Al‐Maadeed

et al. 2019

Neural Process Lett

245

100

View full text Add to dashboard Cite

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“…The method uses FCN with a shift layer. In [40], an image inpainting model named PEPSI which overcomes the limitation of the two-stage coarse-tofine network using the joint learning scheme is proposed. In study [41], a two-stage adversarial model EdgeConnect that comprises of an edge generator followed by an image completion network is proposed.…”

Section: Related Workmentioning

confidence: 99%

Region-Based Removal of Thermal Reflection Using Pruned Fully Convolutional Network

et al. 2020

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In general, an image obtained from a thermal camera often has a mirror reflection or shadow reflected off the ground around an object, which is referred to as thermal reflection. Sometimes the thermal reflections are connected to their objects in images, which makes it difficult to detect or recognize the object only. Thermal reflections sometimes occur on the wall near an object and are detected as another object when they are not connected to the object. Furthermore, the size of thermal reflection and pixel value significantly vary with the medium of the reflected range and the surrounding temperature. In these cases, the patterns and pixel values of thermal reflection and the object become similar and difficult to distinguish. However, there are insufficient studies on removing the thermal reflection of various kinds of objects in diverse environments. Therefore, in this paper, we propose a pruned fully convolutional network (PFCN)-based method for removing the thermal reflection of an object using the surrounding information when image transformation is performed only within the region of an object. When experiments were conducted using self-collected databases (Dongguk thermal image database (DTh-DB) and Dongguk items & vehicles database (DI&V-DB)) and open databases, the method proposed herein exhibited more outstanding performance in removing thermal reflection when compared with the state-of-the-art methods. INDEX TERMS Thermal image, image transform, thermal reflection removal, pruned fully convolutional network.

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“…Image inpainting is a computer vision technique that has a broad definition. It is a process that restores damaged or lost part of an image, insert an object into or remove an object from an image as the human eye could not understand it [1] [2]. According to its definition, there are many applications for it; for example, it could be used for the restoration of old images, editing or composition of an image, etc.…”

Section: Introductionmentioning

confidence: 99%

Image Inpainting by Adaptive Fusion of Variable Spline Interpolations

Nabizadeh

Ghorbanzade

Karimi

et al. 2020

2020 25th International Computer Conference, Computer Society of Iran (CSICC)

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There are many methods for image enhancement. Image inpainting is one of them which could be used in reconstruction and restoration of scratch images or editing images by adding or removing objects. According to its application, different algorithmic and learning methods are proposed. In this paper, the focus is on applications, which enhance the old and historical scratched images. For this purpose, we proposed an adaptive spline interpolation. In this method, a different number of neighbors in four directions are considered for each pixel in the lost block. In the previous methods, predicting the lost pixels that are on edges is the problem. To address this problem, we consider horizontal and vertical edge information. If the pixel is located on an edge, then we use the predicted value in that direction. In other situations, irrelevant predicted values are omitted, and the average of rest values is used as the value of the missing pixel. The method evaluates by PSNR and SSIM metrics on the Kodak dataset. The results show improvement in PSNR and SSIM compared to similar procedures. Also, the run time of the proposed method outperforms others.

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PEPSI++: Fast and Lightweight Network for Image Inpainting

Cited by 88 publications

References 22 publications

Image Inpainting: A Review

Image Inpainting: A Review

Region-Based Removal of Thermal Reflection Using Pruned Fully Convolutional Network

Image Inpainting by Adaptive Fusion of Variable Spline Interpolations

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