Upsampling Real-Time, Low-Resolution CCTV Videos Using Generative Adversarial Networks

Hazra, Debapriya; Byun, Yung-Cheol

doi:10.3390/electronics9081312

Cited by 7 publications

(3 citation statements)

References 26 publications

(39 reference statements)

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“…In addition, the size of the mugshots (972 × 544 pixels) and security camera videos (352 × 288 pixels) might seem small. However, footage from CCTV is usually low-resolution and low-quality to the point that quality enhancement techniques based on DL are emerging [ 68 ]. Therefore, we find the proposed dataset representative of real life.…”

Section: Resultsmentioning

confidence: 99%

FRMDB: Face Recognition Using Multiple Points of View

Contardo

Sernani

Tomassini

et al. 2023

Sensors

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Although face recognition technology is currently integrated into industrial applications, it has open challenges, such as verification and identification from arbitrary poses. Specifically, there is a lack of research about face recognition in surveillance videos using, as reference images, mugshots taken from multiple Points of View (POVs) in addition to the frontal picture and the right profile traditionally collected by national police forces. To start filling this gap and tackling the scarcity of databases devoted to the study of this problem, we present the Face Recognition from Mugshots Database (FRMDB). It includes 28 mugshots and 5 surveillance videos taken from different angles for 39 distinct subjects. The FRMDB is intended to analyze the impact of using mugshots taken from multiple points of view on face recognition on the frames of the surveillance videos. To validate the FRMDB and provide a first benchmark on it, we ran accuracy tests using two CNNs, namely VGG16 and ResNet50, pre-trained on the VGGFace and VGGFace2 datasets for the extraction of face image features. We compared the results to those obtained from a dataset from the related literature, the Surveillance Cameras Face Database (SCFace). In addition to showing the features of the proposed database, the results highlight that the subset of mugshots composed of the frontal picture and the right profile scores the lowest accuracy result among those tested. Therefore, additional research is suggested to understand the ideal number of mugshots for face recognition on frames from surveillance videos.

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Section: Resultsmentioning

confidence: 99%

FRMDB: Face Recognition Using Multiple Points of View

Contardo

Sernani

Tomassini

et al. 2023

Sensors

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“…Therefore, it is important to restore the original image in super-resolution. This is currently being implemented in many applications, such as closed-circuit television surveillance [3] and security systems [4], satellite remote sensing [5], medical imaging [6,7] atmospheric monitoring [8], and robotics [9].…”

Section: Introductionmentioning

confidence: 99%

Deep Residual Dense Network for Single Image Super-Resolution

Musunuri

Kwon

2021

Electronics

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In this paper, we propose a deep residual dense network (DRDN) for single image super- resolution. Based on human perceptual characteristics, the residual in residual dense block strategy (RRDB) is exploited to implement various depths in network architectures. The proposed model exhibits a simple sequential structure comprising residual and dense blocks with skip connections. It improves the stability and computational complexity of the network, as well as the perceptual quality. We adopt a perceptual metric to learn and assess the quality of the reconstructed images. The proposed model is trained with the Diverse2k dataset, and the performance is evaluated using standard datasets. The experimental results confirm that the proposed model exhibits superior performance, with better reconstruction results and perceptual quality than conventional methods.

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“…Image super-resolution plays a vital role in the field of image and computer visionbased applications because the high quality or high-resolution (HR) images have more pixel density level and contains more detailed information. The detailed information is applied in various fields of computer vision and image processing tasks, such as image restoration [1], security surveillance [2], closed-circuit television surveillance [3], and security systems [4], object recognition [5], object detection [6], satellite imaging [7], remote sensing imagery [8][9][10], autonomous driverless car [11], medical imaging [12][13][14], and atmospheric monitoring [15].…”

Section: Introductionmentioning

confidence: 99%

Multi-Path Deep CNN with Residual Inception Network for Single Image Super-Resolution

et al. 2021

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Recent research on single-image super-resolution (SISR) using deep convolutional neural networks has made a breakthrough and achieved tremendous performance. Despite their significant progress, numerous convolutional neural networks (CNN) are limited in practical applications, owing to the requirement of the heavy computational cost of the model. This paper proposes a multi-path network for SISR, known as multi-path deep CNN with residual inception network for single image super-resolution. In detail, a residual/ResNet block with an Inception block supports the main framework of the entire network architecture. In addition, remove the batch normalization layer from the residual network (ResNet) block and max-pooling layer from the Inception block to further reduce the number of parameters to preventing the over-fitting problem during the training. Moreover, a conventional rectified linear unit (ReLU) is replaced with Leaky ReLU activation function to speed up the training process. Specifically, we propose a novel two upscale module, which adopts three paths to upscale the features by jointly using deconvolution and upsampling layers, instead of using single deconvolution layer or upsampling layer alone. The extensive experimental results on image super-resolution (SR) using five publicly available test datasets, which show that the proposed model not only attains the higher score of peak signal-to-noise ratio/structural similarity index matrix (PSNR/SSIM) but also enables faster and more efficient calculations against the existing image SR methods. For instance, we improved our method in terms of overall PSNR on the SET5 dataset with challenging upscale factor 8× as 1.88 dB over the baseline bicubic method and reduced computational cost in terms of number of parameters 62% by deeply-recursive convolutional neural network (DRCN) method.

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Upsampling Real-Time, Low-Resolution CCTV Videos Using Generative Adversarial Networks

Cited by 7 publications

References 26 publications

FRMDB: Face Recognition Using Multiple Points of View

FRMDB: Face Recognition Using Multiple Points of View

Deep Residual Dense Network for Single Image Super-Resolution

Multi-Path Deep CNN with Residual Inception Network for Single Image Super-Resolution

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