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Deep Learning for Image Super-resolution:

A Survey

Zhihao Wang, Jian Chen, Steven C.H. Hoi, Fellow, IEEE

Abstract—Image Super-Resolution (SR) is an important class of image processing techniques to enhance the resolution of images

and videos in computer vision. Recent years have witnessed remarkable progress of image super-resolution using deep learning

techniques. This article aims to provide a comprehensive survey on recent advances of image super-resolution using deep learning

approaches. In general, we can roughly group the existing studies of SR techniques into three major categories: supervised SR,

unsupervised SR, and domain-speciﬁc SR. In addition, we also cover some other important issues, such as publicly available

benchmark datasets and performance evaluation metrics. Finally, we conclude this survey by highlighting several future directions and

open issues which should be further addressed by the community in the future.

Index Terms—Image Super-resolution, Deep Learning, Convolutional Neural Networks (CNN), Generative Adversarial Nets (GAN)

1 INTRODUCTION

MAGE super-resolution (SR), which refers to the process

of recovering high-resolution (HR) images from low-

resolution (LR) images, is an important class of image

processing techniques in computer vision and image pro-

cessing. It enjoys a wide range of real-world applications,

such as medical imaging [1], [2], [3], surveillance and secu-

rity [4], [5]), amongst others. Other than improving image

perceptual quality, it also helps to improve other computer

vision tasks [6], [7], [8], [9]. In general, this problem is very

challenging and inherently ill-posed since there are always

multiple HR images corresponding to a single LR image.

In literature, a variety of classical SR methods have been

proposed, including prediction-based methods [10], [11],

[12], edge-based methods [13], [14], statistical methods [15],

[16], patch-based methods [13], [17], [18], [19] and sparse

representation methods [20], [21], etc.

With the rapid development of deep learning techniques

in recent years, deep learning based SR models have been

actively explored and often achieve the state-of-the-art per-

formance on various benchmarks of SR. A variety of deep

learning methods have been applied to tackle SR tasks, rang-

ing from the early Convolutional Neural Networks (CNN)

based method (e.g., SRCNN [22], [23]) to recent promising

SR approaches using Generative Adversarial Nets (GAN)

[24] (e.g., SRGAN [25]). In general, the family of SR al-

gorithms using deep learning techniques differ from each

other in the following major aspects: different types of

network architectures [26], [27], [28], different types of loss

functions [8], [29], [30], different types of learning principles

• Corresponding author: Steven C.H. Hoi is currently with Salesforce

Research Asia, and also a faculty member (on leave) of the School

of Information Systems, Singapore Management University, Singapore.

Email: shoi@salesforce.com or chhoi@smu.edu.sg.

• Z. Wang is with the South China University of Technology, China. E-mail:

ptkin@outlook.com. This work was done when he was a visiting student

with Dr Hoi’s group at the School of Information Systems, Singapore

Management University, Singapore.

• J. Chen is with the South China University of Technology, China. E-mail:

ellachen@scut.edu.cn.

and strategies [8], [31], [32], etc.

In this paper, we give a comprehensive overview of re-

cent advances in image super-resolution with deep learning.

Although there are some existing SR surveys in literature,

our work differs in that we are focused in deep learning

based SR techniques, while most of the earlier works [33],

[34], [35], [36] aim at surveying traditional SR algorithms or

some studies mainly concentrate on providing quantitative

evaluations based on full-reference metrics or human visual

perception [37], [38]. Unlike the existing surveys, this survey

takes a unique deep learning based perspective to review

the recent advances of SR techniques in a systematic and

comprehensive manner.

The main contributions of this survey are three-fold:

1) We give a comprehensive review of image super-

resolution techniques based on deep learning, in-

cluding problem settings, benchmark datasets, per-

formance metrics, a family of SR methods with deep

learning, domain-speciﬁc SR applications, etc.

2) We provide a systematic overview of recent ad-

vances of deep learning based SR techniques in a

hierarchical and structural manner, and summarize

the advantages and limitations of each component

for an effective SR solution.

3) We discuss the challenges and open issues, and

identify the new trends and future directions to

provide an insightful guidance for the community.

In the following sections, we will cover various aspects

of recent advances in image super-resolution with deep

learning. Fig. 1 shows the taxonomy of image SR to be

covered in this survey in a hierarchically-structured way.

Section 2 gives the problem deﬁnition and reviews the

mainstream datasets and evaluation metrics. Section 3 ana-

lyzes main components of supervised SR modularly. Section

4 gives a brief introduction to unsupervised SR methods.

Section 5 introduces some popular domain-speciﬁc SR ap-

plications, and Section 6 discusses future directions and

open issues.

arXiv:1902.06068v2 [cs.CV] 8 Feb 2020

Performance Evaluation Unsupervised Image Super-resolution Domain-specific Applications

Image Super-resolution

Model Frameworks Upsampling Methods Learning Strategies Other Improvements

Supervised Image Super-resolution

Network Design

Context-wise Network

Fusion

Data Augmentation

Multi-task Learning

Network Interpolation

Self-ensemble

Loss Functions:

- Pixel Loss

- Content Loss

- Texture Loss

- Adversarial Loss

- Cycle Consistency Loss

- Total Variation Loss

- Prior-based Loss

Batch Normalization

Curriculum Learning

Multi-supervision

Residual Learning

Recursive Learning

Multi-path Learning

Dense Connections

Attention Mechanism

Advanced Convolution

Region-recursive Learning

Pyramid Pooling

Wavelet Transformation

xUnit

Desubpixel

Interpolation-

based Methods:

- Nearest Neighbor

- Bilinear

- Bicubic

- Others

Learning-based Methods:

- Transposed Convolution

- Sub-pixel Layer

- Meta Upscale Module

Pre-upsampling SR

Post-upsampling SR

Progressive Upsampling SR

Iterative Up-and-down

Sampling SR

Benchmark Datasets

Performance Metric:

- Objective Methods: PSNR, SSIM, etc.

- Subjective Methods: MOS

- Task-based Evaluation

- Learning-based Perceptual Quality

- Other Methods

Operating Channels

Zero-shot Super-resolution

Weakly-supervised Super-resolution:

- Learned Degradation

- Cycle-in-cycle Super-resolution

Deep Image Prior

Depth Map Super-resolution

Face Image Super-resolution

Hyperspectral Image Super-resolution

Real-world Image Super-resolution

Video Super-resolution

Other Applications

Fig. 1. Hierarchically-structured taxonomy of this survey.

2 PROBLEM SETTING AND TERMINOLOGY

2.1 Problem Deﬁnitions

Image super-resolution aims at recovering the correspond-

ing HR images from the LR images. Generally, the LR image

is modeled as the output of the following degradation:

= D(I

; δ), (1)

where D denotes a degradation mapping function, I

the corresponding HR image and δ is the parameters of

the degradation process (e.g., the scaling factor or noise).

Generally, the degradation process (i.e., D and δ) is un-

known and only LR images are provided. In this case, also

known as blind SR, researchers are required to recover an

HR approximation

of the ground truth HR image I

from

the LR image I

, following:

= F(I

; θ), (2)

where F is the super-resolution model and θ denotes the

parameters of F.

Although the degradation process is unknown and can

be affected by various factors (e.g., compression artifacts,

anisotropic degradations, sensor noise and speckle noise),

researchers are trying to model the degradation mapping.

Most works directly model the degradation as a single

downsampling operation, as follows:

D(I

; δ) = (I

) ↓

, {s} ⊂ δ, (3)

where ↓

is a downsampling operation with the scaling

factor s. As a matter of fact, most datasets for generic SR are

built based on this pattern, and the most commonly used

downsampling operation is bicubic interpolation with anti-

aliasing. However, there are other works [39] modelling the

degradation as a combination of several operations:

D(I

; δ) = (I

⊗ κ) ↓

, {κ, s, ς} ⊂ δ, (4)

where I

⊗ κ represents the convolution between a blur

kernel κ and the HR image I

, and n

is some additive

white Gaussian noise with standard deviation ς. Compared

to the naive deﬁnition of Eq. 3, the combinative degradation

pattern of Eq. 4 is closer to real-world cases and has been

shown to be more beneﬁcial for SR [39].

To this end, the objective of SR is as follows:

θ = arg min

, I

) + λΦ(θ), (5)

where L(

, I

) represents the loss function between the

generated HR image

and the ground truth image I

, Φ(θ)

is the regularization term and λ is the tradeoff parameter.

Although the most popular loss function for SR is pixel-wise

mean squared error (i.e., pixel loss), more powerful models

tend to use a combination of multiple loss functions, which

will be covered in Sec. 3.4.1.

2.2 Datasets for Super-resolution

Today there are a variety of datasets available for image

super-resolution, which greatly differ in image amounts,

quality, resolution, and diversity, etc. Some of them provide

LR-HR image pairs, while others only provide HR images,

in which case the LR images are typically obtained by imre-

size function with default settings in MATLAB (i.e., bicubic

interpolation with anti-aliasing). In Table 1 we list a number

of image datasets commonly used by the SR community,

x->y 低分辨率到高分辨率

高分辨率图像降采样得到低分辨率图像

高分辨率图像和未知模板进行卷积并加高斯噪声生成低分辨率图像

优化损失函数

TABLE 1

List of public image datasets for super-resolution benchmarks.

Dataset Amount Avg. Resolution Avg. Pixels Format Category Keywords

BSDS300 [40] 300 (435, 367) 154, 401 JPG animal, building, food, landscape, people, plant, etc.

BSDS500 [41] 500 (432, 370) 154, 401 JPG animal, building, food, landscape, people, plant, etc.

DIV2K [42] 1000 (1972, 1437) 2, 793, 250 PNG environment, ﬂora, fauna, handmade object, people, scenery, etc.

General-100 [43] 100 (435, 381) 181, 108 BMP animal, daily necessity, food, people, plant, texture, etc.

L20 [44] 20 (3843, 2870) 11, 577, 492 PNG animal, building, landscape, people, plant, etc.

Manga109 [45] 109 (826, 1169) 966, 011 PNG manga volume

OutdoorScene [46] 10624 (553, 440) 249, 593 PNG animal, building, grass, mountain, plant, sky, water

PIRM [47] 200 (617, 482) 292, 021 PNG environments, ﬂora, natural scenery, objects, people, etc.

Set5 [48] 5 (313, 336) 113, 491 PNG baby, bird, butterﬂy, head, woman

Set14 [49] 14 (492, 446) 230, 203 PNG humans, animals, insects, ﬂowers, vegetables, comic, slides, etc.

T91 [21] 91 (264, 204) 58, 853 PNG car, ﬂower, fruit, human face, etc.

Urban100 [50] 100 (984, 797) 774, 314 PNG architecture, city, structure, urban, etc.

and speciﬁcally indicate their amounts of HR images, aver-

age resolution, average numbers of pixels, image formats,

and category keywords.

Besides these datasets, some datasets widely used for

other vision tasks are also employed for SR, such as Ima-

geNet [51], MS-COCO [52], VOC2012 [53], CelebA [54]. In

addition, combining multiple datasets for training is also

popular, such as combining T91 and BSDS300 [26], [27], [55],

[56], combining DIV2K and Flickr2K [31], [57].

2.3 Image Quality Assessment

Image quality refers to visual attributes of images and fo-

cuses on the perceptual assessments of viewers. In general,

image quality assessment (IQA) methods include subjective

methods based on humans’ perception (i.e., how realistic

the image looks) and objective computational methods.

The former is more in line with our need but often time-

consuming and expensive, thus the latter is currently the

mainstream. However, these methods aren’t necessarily con-

sistent between each other, because objective methods are

often unable to capture the human visual perception very

accurately, which may lead to large difference in IQA results

[25], [58].

In addition, the objective IQA methods are further di-

vided into three types [58]: full-reference methods perform-

ing assessment using reference images, reduced-reference

methods based on comparisons of extracted features, and

no-reference methods (i.e., blind IQA) without any refer-

ence images. Next we’ll introduce several most commonly

used IQA methods covering both subjective methods and

objective methods.

2.3.1 Peak Signal-to-Noise Ratio

Peak signal-to-noise ratio (PSNR) is one of the most popular

reconstruction quality measurement of lossy transforma-

tion (e.g., image compression, image inpainting). For image

super-resolution, PSNR is deﬁned via the maximum pixel

value (denoted as L) and the mean squared error (MSE)

between images. Given the ground truth image I with N

pixels and the reconstruction

I, the PSNR between I and

are deﬁned as follows:

PSNR = 10 · log

(

i=1

(I(i) −

I(i))

), (6)

where L equals to 255 in general cases using 8-bit repre-

sentations. Since the PSNR is only related to the pixel-level

MSE, only caring about the differences between correspond-

ing pixels instead of visual perception, it often leads to poor

performance in representing the reconstruction quality in

real scenes, where we’re usually more concerned with hu-

man perceptions. However, due to the necessity to compare

with literature works and the lack of completely accurate

perceptual metrics, PSNR is still currently the most widely

used evaluation criteria for SR models.

2.3.2 Structural Similarity

Considering that the human visual system (HVS) is highly

adapted to extract image structures [59], the structural

similarity index (SSIM) [58] is proposed for measuring the

structural similarity between images, based on independent

comparisons in terms of luminance, contrast, and structures.

For an image I with N pixels, the luminance µ

and contrast

are estimated as the mean and standard deviation of

the image intensity, respectively, i.e., µ

i=1

I(i) and

= (

N−1

i=1

(I(i) − µ

)

, where I(i) represents the

intensity of the i-th pixel of image I. And the comparisons

on luminance and contrast, denoted as C

(I,

I) and C

(I,

respectively, are given by:

(I,

I) =

2µ

+ C

+ µ

+ C

, (7)

(I,

I) =

2σ

+ C

+ σ

+ C

, (8)

where C

= (k

and C

= (k

are constants for

avoiding instability, k

 1 and k

 1.

Besides, the image structure is represented by the nor-

malized pixel values (i.e., (I − µ

)/σ

), whose correlations

(i.e., inner product) measure the structural similarity, equiv-

L为图片像素值可以取到的最大值。

8bit图则为255

K1、K2怎么取？

alent to the correlation coefﬁcient between I and

I. Thus the

structure comparison function C

(I,

I) is deﬁned as:

N − 1

i=1

(I(i) − µ

)(

I(i) − µ

), (9)

(I,

I) =

+ C

, (10)

where σ

is the covariance between I and

I, and C

is a

constant for stability.

Finally, the SSIM is given by:

SSIM(I,

I) = [C

(I,

I)]

(I,

I)]

(I,

I)]

, (11)

where α, β, γ are control parameters for adjusting the

relative importance.

Since the SSIM evaluates the reconstruction quality from

the perspective of the HVS, it better meets the requirements

of perceptual assessment [60], [61], and is also widely used.

2.3.3 Mean Opinion Score

Mean opinion score (MOS) testing is a commonly used

subjective IQA method, where human raters are asked to

assign perceptual quality scores to tested images. Typically,

the scores are from 1 (bad) to 5 (good). And the ﬁnal MOS

is calculated as the arithmetic mean over all ratings.

Although the MOS testing seems a faithful IQA method,

it has some inherent defects, such as non-linearly perceived

scales, biases and variance of rating criteria. In reality, there

are some SR models performing poorly in common IQA

metrics (e.g., PSNR) but far exceeding others in terms of

perceptual quality, in which case the MOS testing is the

most reliable IQA method for accurately measuring the

perceptual quality [8], [25], [46], [62], [63], [64], [65].

2.3.4 Learning-based Perceptual Quality

In order to better assess the image perceptual quality while

reducing manual intervention, researchers try to assess the

perceptual quality by learning on large datasets. Speciﬁcally,

Ma et al. [66] and Talebi et al. [67] propose no-reference

Ma and NIMA, respectively, which are learned from visual

perceptual scores and directly predict the quality scores

without ground-truth images. In contrast, Kim et al. [68] pro-

pose DeepQA, which predicts visual similarity of images by

training on triplets of distorted images, objective error maps,

and subjective scores. And Zhang et al. [69] collect a large-

scale perceptual similarity dataset, evaluate the perceptual

image patch similarity (LPIPS) according to the difference in

deep features by trained deep networks, and show that the

deep features learned by CNNs model perceptual similarity

much better than measures without CNNs.

Although these methods exhibit better performance on

capturing human visual perception, what kind of perceptual

quality we need (e.g., more realistic images, or consistent

identity to the original image) remains a question to be

explored, thus the objective IQA methods (e.g., PSNR, SSIM)

are still the mainstreams currently.

2.3.5 Task-based Evaluation

According to the fact that SR models can often help other

vision tasks [6], [7], [8], [9], evaluating reconstruction per-

formance by means of other tasks is another effective way.

Speciﬁcally, researchers feed the original and the recon-

structed HR images into trained models, and evaluate the

reconstruction quality by comparing the impacts on the pre-

diction performance. The vision tasks used for evaluation

include object recognition [8], [70], face recognition [71], [72],

face alignment and parsing [30], [73], etc.

2.3.6 Other IQA Methods

In addition to above IQA methods, there are other less

popular SR metrics. The multi-scale structural similarity

(MS-SSIM) [74] supplies more ﬂexibility than single-scale

SSIM in incorporating the variations of viewing conditions.

The feature similarity (FSIM) [75] extracts feature points

of human interest based on phase congruency and image

gradient magnitude to evaluate image quality. The Natural

Image Quality Evaluator (NIQE) [76] makes use of mea-

surable deviations from statistical regularities observed in

natural images, without exposure to distorted images.

Recently, Blau et al. [77] prove mathematically that dis-

tortion (e.g., PSNR, SSIM) and perceptual quality (e.g.,

MOS) are at odds with each other, and show that as the

distortion decreases, the perceptual quality must be worse.

Thus how to accurately measure the SR quality is still an

urgent problem to be solved.

2.4 Operating Channels

In addition to the commonly used RGB color space, the

YCbCr color space is also widely used for SR. In this space,

images are represented by Y, Cb, Cr channels, denoting

the luminance, blue-difference and red-difference chroma

components, respectively. Although currently there is no

accepted best practice for performing or evaluating super-

resolution on which space, earlier models favor operating

on the Y channel of YCbCr space [26], [43], [78], [79], while

more recent models tend to operate on RGB channels [28],

[31], [57], [70]. It is worth noting that operating (training or

evaluation) on different color spaces or channels can make

the evaluation results differ greatly (up to 4 dB) [23].

2.5 Super-resolution Challenges

In this section, we will brieﬂy introduce two most popular

challenges for image SR, NTIRE [80] and PIRM [47], [81].

NTIRE Challenge. The New Trends in Image Restora-

tion and Enhancement (NTIRE) challenge [80] is in conjunc-

tion with CVPR and includes multiple tasks like SR, denois-

ing and colorization. For image SR, the NTIRE challenge

is built on the DIV2K [42] dataset and consists of bicubic

downscaling tracks and blind tracks with realistic unknown

degradation. These tracks differs in degradations and scal-

ing factors, and aim to promote the SR research under both

ideal conditions and real-world adverse situations.

PIRM Challenge. The Perceptual Image Restoration and

Manipulation (PIRM) challenges are in conjunction with

ECCV and also includes multiple tasks. In contrast to

NTIRE, one sub-challenge [47] of PIRM focuses on the trade-

off between generation accuracy and perceptual quality, and

C3怎么求？

the other [81] focuses on SR on smartphones. As is well-

known [77], the models target for distortion frequently pro-

duce visually unpleasing results, while the models target for

perceptual quality performs poorly on information ﬁdelity.

Speciﬁcally, the PIRM divided the perception-distortion

plane into three regions according to thresholds on root

mean squared error (RMSE). In each region, the winning

algorithm is the one that achieves the best perceptual quality

[77], evaluated by NIQE [76] and Ma [66]. While in the

other sub-challenge [81], SR on smartphones, participants

are asked to perform SR with limited smartphone hardwares

(including CPU, GPU, RAM, etc.), and the evaluation met-

rics include PSNR, MS-SSIM and MOS testing. In this way,

PIRM encourages advanced research on the perception-

distortion tradeoff, and also drives lightweight and efﬁcient

image enhancement on smartphones.

3 SUPERVISED SUPER-RESOLUTION

Nowadays researchers have proposed a variety of super-

resolution models with deep learning. These models fo-

cus on supervised SR, i.e., trained with both LR images

and corresponding HR images. Although the differences

between these models are very large, they are essentially

some combinations of a set of components such as model

frameworks, upsampling methods, network design, and

learning strategies. From this perspective, researchers com-

bine these components to build an integrated SR model for

ﬁtting speciﬁc purposes. In this section, we concentrate on

modularly analyzing the fundamental components (as Fig.

1 shows) instead of introducing each model in isolation, and

summarizing their advantages and limitations.

3.1 Super-resolution Frameworks

Since image super-resolution is an ill-posed problem, how

to perform upsampling (i.e., generating HR output from LR

input) is the key problem. Although the architectures of

existing models vary widely, they can be attributed to four

model frameworks (as Fig. 2 shows), based on the employed

upsampling operations and their locations in the model.

3.1.1 Pre-upsampling Super-resolution

On account of the difﬁculty of directly learning the mapping

from low-dimensional space to high-dimensional space, uti-

lizing traditional upsampling algorithms to obtain higher-

resolution images and then reﬁning them using deep neural

networks is a straightforward solution. Thus Dong et al.

[22], [23] ﬁrstly adopt the pre-upsampling SR framework

(as Fig. 2a shows) and propose SRCNN to learn an end-to-

end mapping from interpolated LR images to HR images.

Speciﬁcally, the LR images are upsampled to coarse HR

images with the desired size using traditional methods (e.g.,

bicubic interpolation), then deep CNNs are applied on these

images for reconstructing high-quality details.

Since the most difﬁcult upsampling operation has been

completed, CNNs only need to reﬁne the coarse images,

which signiﬁcantly reduces the learning difﬁculty. In ad-

dition, these models can take interpolated images with

arbitrary sizes and scaling factors as input, and give re-

ﬁned results with comparable performance to single-scale

conv

upsample

conv

(a) Pre-upsampling SR

upsample

conv

(b) Post-upsampling SR

upsample

conv

upsample

conv

upsample

downsample

upsample

downsample

(d) Iterative up-and-down Sampling SR

Fig. 2. Super-resolution model frameworks based on deep learning. The

cube size represents the output size. The gray ones denote predeﬁned

upsampling, while the green, yellow and blue ones indicate learnable

upsampling, downsampling and convolutional layers, respectively. And

the blocks enclosed by dashed boxes represent stackable modules.

SR models [26]. Thus it has gradually become one of the

most popular frameworks [55], [56], [82], [83], and the main

differences between these models are the posterior model

design (Sec. 3.3) and learning strategies (Sec. 3.4). However,

the predeﬁned upsampling often introduce side effects (e.g.,

noise ampliﬁcation and blurring), and since most operations

are performed in high-dimensional space, the cost of time

and space is much higher than other frameworks [43], [84].

3.1.2 Post-upsampling Super-resolution

In order to improve the computational efﬁciency and make

full use of deep learning technology to increase resolution

automatically, researchers propose to perform most compu-

tation in low-dimensional space by replacing the predeﬁned

upsampling with end-to-end learnable layers integrated at

the end of the models. In the pioneer works [43], [84]

of this framework, namely post-upsampling SR as Fig. 2b

shows, the LR input images are fed into deep CNNs without

increasing resolution, and end-to-end learnable upsampling

layers are applied at the end of the network.

小变大

先对原始LR图像使用传统

方法上采样扩大到任意尺

度。再使用deep learning

的方法进行修正。

缺点：传统方法上采样会

放大噪声，造成模糊。同

时图像变大，使网络修正

时间边长。

先在原始LR图像学习特征，再在

最后进行上采样恢复成HR

渐进上采样

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