Fine-grained Adversarial Image Inpainting with Super Resolution

Li, Yang; Jiang, Bitao; Lu, Yao; Shen, Li

doi:10.1109/ijcnn.2019.8852241

Cited by 4 publications

(3 citation statements)

References 20 publications

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“…In this test, four videos are considered. The state‐of‐the‐art algorithms of Giryes and Elad [40], Zarif et al [41], Newson et al [20], Janardhana Rao et al [21] and Yang Li et al [22] are used as the comparison baseline, as shown in Table 3. As mentioned in Table 3, three loss rates are considered, viz., 10, 25 and 45% for each video sequence.…”

Section: Resultsmentioning

confidence: 99%

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Dual‐tree complex wavelet transform and super‐resolution based video inpainting application to object removal and error concealment

Tudavekar

Patil

Saraf

2020

CAAI trans. intell. technol.

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Video inpainting is a technique that fills in the missing regions or gaps in a video by using its known pixels. The existing video inpainting algorithms are computationally expensive and introduce seam in the target region that arises due to variation in brightness or contrast of the patches. To overcome these drawbacks, the authors propose a novel two‐stage framework. In the first step, sub‐bands of wavelets of a low‐resolution image are obtained using the dual‐tree complex wavelet transform. Criminisi algorithm and auto‐regression technique are then applied to these sub‐bands to inpaint the missing regions. The fuzzy logic‐based histogram equalisation is used to further enhance the image by preserving the image brightness and improve the local contrast. In the second step, the image is enhanced using super‐resolution technique. The process of down‐sampling, inpainting and subsequently enhancing the video using the super‐resolution technique reduces the video inpainting time. The framework is tested on video sequences by comparing and analysing the structural similarity index matrix, peak‐signal‐to‐noise ratio, visual information fidelity in pixel domain and execution time with the state‐of‐the‐art algorithms. The experimental analysis gives visually pleasing results for object removal and error concealment.

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Section: Resultsmentioning

confidence: 99%

“…In recent years, to improve the accuracy of the video inpainting, deep learning based image inpainting methods use SR. These methods produce accurate results but the runtime is more because these models need to be trained using external training data [22–24].…”

Section: Introductionmentioning

confidence: 99%

Dual‐tree complex wavelet transform and super‐resolution based video inpainting application to object removal and error concealment

Tudavekar

Patil

Saraf

2020

CAAI trans. intell. technol.

View full text Add to dashboard Cite

show abstract

“…To handle the information gap between the LR and HR data, one can stack more convolutional layers in the encoder-decoder architecture [20], [61]. However, more layers of continuous convolutions result in the loss of information which may be essential for synthesizing the SRd image [20], [62]. Therefore, we decompose the SR problem into multiple stages.…”

Section: A Multi-stage Gan (Msgan)mentioning

confidence: 99%

Joint Super-Resolution and Head Pose Estimation for Extreme Low-Resolution Faces

et al. 2023

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State-of-the-art deep learning-based Head Pose Estimation (HPE) techniques have reached spectacular performance on High-Resolution (HR) face images. However, they still fail to achieve expected performance on low-resolution images at large scales. This work presents an end-to-end HPE framework assisted by a Face Super-Resolution (FSR) algorithm. The proposed FSR model is specifically guided to enhance the HPE performance rather than considering FSR as an independent task. To this end, we utilized a Multi-Stage Generative Adversarial Network (MSGAN) which benefit from a pose-aware adversarial loss and head pose estimation feedback to generate super-resolved images that are properly aligned for HPE. Also, we propose a degradation strategy rather than simple down-sampling approach to mimic the diverse properties of real-world Low-Resolution (LR) images. We evaluate the performance of our proposed method on both synthetic and real-world LR datasets and show the superiority of our approach in both visual and HPE metrics on the AFLW2000, BIWI, and WiderFace Datasets.INDEX TERMS Head pose estimation (HPE), face super-resolution (FSR), multi-stage generative adversarial networks (MSGAN), low-resolution (LR) face images.

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Dual-Tree Complex Wavelet Transform and Deep CNN-Based Super-Resolution for Video Inpainting with Application to Object Removal and Error Concealment

Tudavekar

Patil

Saraf³

2021

Computational Intelligence Methods for Super-Resolution in Image Processing Applications

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Fine-grained Adversarial Image Inpainting with Super Resolution

Cited by 4 publications

References 20 publications

Dual‐tree complex wavelet transform and super‐resolution based video inpainting application to object removal and error concealment

Dual‐tree complex wavelet transform and super‐resolution based video inpainting application to object removal and error concealment

Joint Super-Resolution and Head Pose Estimation for Extreme Low-Resolution Faces

Dual-Tree Complex Wavelet Transform and Deep CNN-Based Super-Resolution for Video Inpainting with Application to Object Removal and Error Concealment

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