Feature Super-Resolution: Make Machine See More Clearly

Tan, Weimin; Yan, Bo; Bare, Bahetiyaer

doi:10.1109/cvpr.2018.00420

Cited by 47 publications

(33 citation statements)

References 7 publications

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“…Other applications of SR include object detection ( Li et al, 2017a ; Tan, Yan & Bare, 2018 ), stereo image SR ( Duan & Xiao, 2019 ; Guo, Chen & Huang, 2019 ; Wang et al, 2019a ), and super-resolution in optical microscopy ( Qiao et al, 2021 ). Overall, SR plays a vital role in multi-disciplines, from medical science, computer vision to satellite imaging and remote sensing.…”

Section: Domain-specific Applications Of Super-resolutionmentioning

confidence: 99%

A comprehensive review of deep learning-based single image super-resolution

Bashir

Wang

Khan

et al. 2021

PeerJ Computer Science

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Image super-resolution (SR) is one of the vital image processing methods that improve the resolution of an image in the field of computer vision. In the last two decades, significant progress has been made in the field of super-resolution, especially by utilizing deep learning methods. This survey is an effort to provide a detailed survey of recent progress in single-image super-resolution in the perspective of deep learning while also informing about the initial classical methods used for image super-resolution. The survey classifies the image SR methods into four categories, i.e., classical methods, supervised learning-based methods, unsupervised learning-based methods, and domain-specific SR methods. We also introduce the problem of SR to provide intuition about image quality metrics, available reference datasets, and SR challenges. Deep learning-based approaches of SR are evaluated using a reference dataset. Some of the reviewed state-of-the-art image SR methods include the enhanced deep SR network (EDSR), cycle-in-cycle GAN (CinCGAN), multiscale residual network (MSRN), meta residual dense network (Meta-RDN), recurrent back-projection network (RBPN), second-order attention network (SAN), SR feedback network (SRFBN) and the wavelet-based residual attention network (WRAN). Finally, this survey is concluded with future directions and trends in SR and open problems in SR to be addressed by the researchers.

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Section: Domain-specific Applications Of Super-resolutionmentioning

confidence: 99%

A comprehensive review of deep learning-based single image super-resolution

Bashir

Wang

Khan

et al. 2021

PeerJ Computer Science

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“…However, the performance is achieved only for large size images having rich object structure and high quality appearance. e resolution of images with small size is low, which limits the learning of discriminative representations, thus leading to identification failure [18]. In this experiment, the impact of down-scaling operation on image representations can be evaluated on Oxford5K dataset [19] by our AQPHT and the widespread deep model of VGG16.…”

Section: Experiments On Scalingmentioning

confidence: 99%

Accurate Quaternion Polar Harmonic Transform for Color Image Analysis

Zhang¹,

Sang

Dai³

2021

Scientific Programming

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Polar harmonic transforms (PHTs) have been applied in pattern recognition and image analysis. But the current computational framework of PHTs has two main demerits. First, some significant color information may be lost during color image processing in conventional methods because they are based on RGB decomposition or graying. Second, PHTs are influenced by geometric errors and numerical integration errors, which can be seen from image reconstruction errors. This paper presents a novel computational framework of quaternion polar harmonic transforms (QPHTs), namely, accurate QPHTs (AQPHTs). First, to holistically handle color images, quaternion-based PHTs are introduced by using the algebra of quaternions. Second, the Gaussian numerical integration is adopted for geometric and numerical error reduction. When compared with CNNs (convolutional neural networks)-based methods (i.e., VGG16) on the Oxford5K dataset, our AQPHT achieves better performance of scaling invariant representation. Moreover, when evaluated on standard image retrieval benchmarks, our AQPHT using smaller dimension of feature vector achieves comparable results with CNNs-based methods and outperforms the hand craft-based methods by 9.6% w.r.t mAP on the Holidays dataset.

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“…Thus, our input and output have the same number of channels. Our SA architecture inspires from [6] which has shown that upsampling and downsampling module within the architecture improves the accuracy in SR. For the activation unit, by following ref [12,29], we prefer the LeakyReLU over ReLU activation whereas we use the linear bottleneck layer as suggested in [23].…”

Section: Spatial Attention (Sa)mentioning

confidence: 99%

HRAN: Hybrid Residual Attention Network for Single Image Super-Resolution

2019

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The extraction and proper utilization of convolution neural network (CNN) features have a significant impact on the performance of image super-resolution (SR). Although CNN features contain both the spatial and channel information, current deep techniques on SR often suffer to maximize performance due to using either the spatial or channel information. Moreover, they integrate such information within a deep or wide network rather than exploiting all the available features, eventually resulting in high computational complexity. To address these issues, we present a binarized feature fusion (BFF) structure that utilizes the extracted features from residual groups (RG) in an effective way. Each residual group (RG) consists of multiple hybrid residual attention blocks (HRAB) that effectively integrates the multiscale feature extraction module and channel attention mechanism in a single block. Furthermore, we use dilated convolutions with different dilation factors to extract multiscale features. We also propose to adopt global, short and long skip connections and residual groups (RG) structure to ease the flow of information without losing important features details. In the paper, we call this overall network architecture as hybrid residual attention network (HRAN). In the experiment, we have observed the efficacy of our method against the state-of-the-art methods for both the quantitative and qualitative comparisons.

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Feature Super-Resolution: Make Machine See More Clearly

Cited by 47 publications

References 7 publications

A comprehensive review of deep learning-based single image super-resolution

A comprehensive review of deep learning-based single image super-resolution

Accurate Quaternion Polar Harmonic Transform for Color Image Analysis

HRAN: Hybrid Residual Attention Network for Single Image Super-Resolution

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