Blind Super-resolution of Faces for Surveillance

Nimisha, T M; Rajagopalan, A. N.

doi:10.1007/978-3-030-74697-1_6

Cited by 5 publications

(2 citation statements)

References 32 publications

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“…Learning-based deep networks give impressive results for individual restoration tasks like deblurring [10]- [25], dehazing [26]- [29], inpainting [30], [31], enhancement [32], [33], superresolution [34]- [40], bokeh rendering [41], inpainting [31], [42] etc. Unsupervised methods relax the need for paired datasets for training a deep net.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Distortion Disentanglement and Knowledge Distillation for Satellite Image Restoration

Kandula

Rajagopalan

2022

IEEE Trans. Geosci. Remote Sensing

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Satellite images are typically subject to multiple distortions. Different factors affect the quality of satellite images, including changes in atmosphere, surface reflectance, sun illumination, viewing geometries etc., limiting its application to downstream tasks. In supervised networks, the availability of paired datasets is a strong assumption. Consequently, many unsupervised algorithms have been proposed to address this problem. These methods synthetically generate a large dataset of degraded images using image formation models. A neural network is then trained with an adversarial loss to discriminate between images from distorted and clean domains. However, these methods yield suboptimal performance when tested on real images that do not necessarily conform to the generation mechanism. Also, they require a large amount of training data and are rendered unsuitable when only a few images are available. We propose a distortion disentanglement and knowledge distillation framework for satellite image restoration to address these important issues. Our algorithm requires only two images: the distorted satellite image to be restored and a reference image with similar semantics. Specifically, we first propose a mechanism to disentangle distortion. This enables us to generate images with varying degrees of distortion using the disentangled distortion and the reference image. We then propose the use of knowledge distillation to train a restoration network using the generated image pairs. As a final step, the distorted image is passed through the restoration network to get the final output. Ablation studies show that our proposed mechanism successfully disentangles distortion. Exhaustive experiments on different timestamps of Google-Earth images and publicly available datasets, LEVIR-CD and SZTAKI, show that our proposed mechanism can tackle a variety of distortions and outperforms existing state-of-the-art restoration methods visually as well as on quantitative metrics.

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Section: Introduction and Related Workmentioning

confidence: 99%

Distortion Disentanglement and Knowledge Distillation for Satellite Image Restoration

Kandula

Rajagopalan

2022

IEEE Trans. Geosci. Remote Sensing

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“…The same scene of a video frame sequence is shown in Figure 1 with different resolutions. Consequently, numerous multimedia applications are expected to embrace it, including surveillance cameras [3], video streaming [4], high-definition television [5], video compression [6], [7], remote sensing [8], and video conferencing [9]. Many life events, like opening a bottle of champagne or seeing lightning, take place quickly and are challenging to watch in real time.…”

Section: Motivation and Research Problemmentioning

confidence: 99%

Deep consecutive attention network for video super-resolution

Saleem

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In the video application, slow motion is visually attractive and gets more attention in video super resolution. To generate the high-resolution (HR)�slow motion video frames from the low-resolution (LR) frames, two sub-tasks are required, including video super-resolution (VSR) and video frame interpolation (VFI). However, the interpolation approach is not successful to extract low level feature attention to get the maximum advantage from the property of space-time relation. To this extent, we propose a deep consecutive attention network-based method. The multi-head attention and an attentive temporal feature module are designed to achieve better prediction of interpolation feature frame. Bi-directional deformable ConvLSTM module aggregates and aligns with the information from the multi-head attention and temporal feature block to improve the quality of video frames. This method synthesizes the HR video frames from LR video frames. The experimental results in terms of PSNR show the proposed method of deep consecutive attention outperforms 0.27 dB and 0.31 dB for Vid4 and SPMC datasets respectively, in average of PSNR compared to state-of-the-art baseline method.

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SEBR: Scharr Edge-Based Regularization Method for Blind Image Deblurring

Bibi

Dawood

2023

Arab J Sci Eng

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Blind Super-resolution of Faces for Surveillance

Cited by 5 publications

References 32 publications

Distortion Disentanglement and Knowledge Distillation for Satellite Image Restoration

Distortion Disentanglement and Knowledge Distillation for Satellite Image Restoration

Deep consecutive attention network for video super-resolution

SEBR: Scharr Edge-Based Regularization Method for Blind Image Deblurring

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