超分辨率重建经典：《Super-Resolution Imaging》

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"Super-Resolution Imaging" 是一本由 Peyman Milanfar 编辑的专业书籍，集中讨论了超分辨率重建技术，是图像处理领域的经典之作。这本书涵盖了所有最新的、最佳的超分辨率算法，对于从事超分辨率研究的工作者极具价值。超分辨率成像是一种图像处理技术，其目标是通过结合多个低分辨率（LR）图像来生成一个高分辨率（HR）图像，从而提高图像的细节和清晰度。这项技术在许多领域都有广泛的应用，包括医学成像、遥感、视频监控和消费电子产品的图像增强等。书中可能涉及的知识点包括： 1. **基础理论**：首先会介绍超分辨率的基本概念，包括图像的采样理论、空间分辨率与频率分辨率的关系，以及Nyquist-Shannon采样定理在超分辨率中的应用。 2. **算法分类**： - **基于插值的超分辨率**：如最近邻插值、双线性插值和多项式插值等，这些方法相对简单，但可能无法完全恢复细节。 - **基于模型的超分辨率**：利用图像的先验知识（如平滑性、边缘保持等）构建图像模型，如基于稀疏表示的超分辨率、基于字典学习的方法等。 - **基于学习的超分辨率**：包括传统机器学习方法和深度学习方法，如基于核函数的映射、自编码器、卷积神经网络（CNN）等，近年来深度学习在超分辨率领域取得了显著的突破。 3. **图像恢复与去噪**：在超分辨率过程中，通常需要先去除图像噪声，这可能涉及到各种去噪算法，如快速傅里叶变换（FFT）、小波分析、非局部均值去噪（NLMeans）等。 4. **优化方法**：超分辨率重建通常是一个非线性优化问题，书中可能会介绍梯度下降法、迭代算法、最小化能量函数等优化策略。 5. **应用实例**：书中可能包含实际案例，展示超分辨率技术如何应用于不同场景，如医学图像增强、天文图像处理等。 6. **评价指标**：如峰值信噪比（PSNR）、结构相似性指数（SSIM）、视觉质量评估等，这些都是衡量超分辨率算法性能的重要标准。 7. **版权与伦理**：书中的章节可能还会涉及在使用和出版过程中对知识产权的尊重和保护，提醒读者在使用或引用他人研究成果时应遵守相关法规。这本书是超分辨率领域的权威著作，对于深入理解和实践超分辨率技术的科研人员来说，是一本不可或缺的参考书。通过阅读本书，读者可以了解到超分辨率技术的最新进展，并掌握实现这些技术所需的关键知识和技能。

Preface

“Enhance 34 to 36. Pan right and pull back. Stop. Enhance 34 to 46. Pull

back. Wait a minute, go right, stop. Enhance 57 to 19. Track 45 left. Stop.

Enhance 15 to 23. Give me a hard copy right there.”

– Harrison Ford in Blade Runner, 1982

If you have never seen the movie Blade Runner, you should. Aside from be-

ing one of the greatest science ﬁction ﬁlms of all time, it is uniquely relevant

to the subject of this book: almost 30 years ago, the opening scene of this

movie foresaw the idea of super-resolution. In the intervening years, a great

deal has transpired: computing power has increased by orders of magnitude,

digital cameras are everywhere, and of course digital displays have become

magniﬁcently detailed. Along with these advances, the public’s expectations

for high-quality imagery has naturally intensiﬁed, often out of proportion with

the state-of-the-art technology. In fact, in the last few years, the visual quality

of captured images and video has not kept pace with these lofty expectations.

By packing increasingly larger number of pixels into ever smaller spaces, and

using less sophisticated optical elements, public, commercial, and oﬃcial users

alike have seen an overall decline in the visual quality of their recorded con-

tent. So despite what might at ﬁrst seem like a losing battle against better

and cheaper sensors, super-resolution technology (and image enhancement

more generally) has really become more relevant than ever. Given that almost

all recorded visual content is now enhanced in one form or another by just

about every digital camera sold today, it is not entirely outrageous then to

believe that before long, super-resolution will become the ”killer application”

for imaging.

Ironically, only two years after the release of Blade Runner,thesemi-

nal paper by Tsai and Huang kick-started the modern idea of computational

super-resolution. While results in sampling theory dating as far back as the

’50s (Yan) and the ’70s (Papoulis) had hinted at the idea, it was Tsai and

Huang who explicitly showed that, at least in theory, it was possible to im-

prove resolution by registering and fusing multiple images. The rest, as they

say, is history. We are fortunate to be able to write a little bit of that history

in this book. In the last ﬁve years or so, the ﬁeld of super-resolution imaging

has truly ﬂourished both academically and commercially. The growing impor-

tance of super-resolution imaging has manifested itself in an explosive growth

in the number of papers in this area and citations to these papers (a few dozen

xvi Preface

in 1994, to more than 500 in 2004, and to more than 2000 in 2008). What has

been missing, however, is a book to not only gather key recent contributions

in one place, but also to serve as a starting point for those interested in this

ﬁeld to begin learning about and exploring the state of the art. This is what

this book hopes to accomplish.

As is probably well-known by now, every super-resolution algorithm ever

developed is sabotaged by at least one spoke of our triumvirate “axis of evil”:

the need for (1) (subpixel) accurate motion estimation, (2) (spatially varying)

deblurring, and (3) robustness to modeling and stochastic errors. To be sure,

these are not independent problems and should ideally be treated in unison

(ambitious graduate students take note!). But realistically, each is suﬃciently

complex as to merit its own section in the library, or at least a couple of nice

chapters in this book. This books gathers contributions that will present the

reader with a snapshot of where the ﬁeld stands, a reasonable idea of where the

ﬁeld is heading—and perhaps where it should be heading. Chapter 1 provides

an introduction to the history of the subject that should be of broad interest.

Indeed, the collection of citations summarized in this chapter is an excellent

wellspring for continued research on super-resolution.

One of the most active areas of work in image and video enhancement

in recent years has been the subject of locally adaptive processing methods,

which are discussed in Chapters 2, 3, 4, and 5. In contrast to globally optimal

methods (treated later in the book), these methods are built on the notion

that processing should be strongly tailored to the local behavior of the given

data. An explicit goal in some cases, and a happy consequence in others,

local processing enables us to largely avoid direct and detailed estimation of

motion. Readers interested in methods for explicit motion estimation will ﬁnd

an excellent overview of modern techniques in Chapter 6.

While motion estimation is typically the ﬁrst step in many super-resolution

algorithms, deblurring is typically the last step. Unfortunately, having been

relegated to the last position has meant that this important aspect of enhance-

ment has not received the respect and attention it deserves. Despite heavy

recent activity in both the image processing and machine vision community,

and some notable successes, deblurring even in its simplest (space-invariant,

known point-spread-function) form is still largely an unsolved problem. Inas-

much as we would like to hope, blur almost never manifests itself in a spatially

uniform fashion. In Chapter 7, the reader will ﬁnd a well-motivated and di-

rect attack at this challenging problem. Despite our best eﬀorts, a sequential

approach to super-resolution consisting of motion estimation, fusion, and de-

blurring will always be subject to the vagaries of the data, the models, and

noise. As such, building robustness into the reconstruction process, as treated

in Chapter 8, is vital if the algorithm is to be practically useful.

As with most inverse problems, super-resolution is highly ill-posed. In the

most general case, the motion between the frames, the blur kernel(s), and the

high-resolution image of interest are three interwoven unknowns that should

ideally be estimated together (rather than sequentially), and whose eﬀect is

Preface xvii

directly felt in the three points of weakness to which I referred earlier. Prin-

cipled Bayesian statistical approaches addressing these issues are presented

in Chapters 9 and 10, where the ever important prior information is brought

to bear on the super-resolution problem. Of course, prior information can be

brought to the table either in “bulk” form as a statistical distribution, or in

more speciﬁc “piecemeal” form as examples. Naively speaking, this distinction

is indeed what leads us to learning-based methods described in Chapter 11.

In the ﬁnal three chapters of the book, we concentrate on applications.

Among the many areas of science to which super-resolution has been success-

fully applied in recent years, medicine and remote sensing have perhaps seen

the most direct impact. In Chapter 12, a novel application of super-resolution

to massive multispectral remote-sensing data sets is detailed. Medical imaging

applications of super-resolution in Chapter 13 discuss two important problems.

In what should be good news to everyone, high resolution X-ray imaging is

made possible at lower radiation dosages thanks to super-resolution. In an-

other application, detailed imaging of the retina is made possible for diagnostic

purposes. Finally, in Chapter 14, a successful commercial application of super-

resolution (in which I was fortunate to have a hand) is discussed. This chapter

is quite informative not only because of the interesting perspective it provides,

but also because of the valuable practical nuggets it imparts to the reader.

It is an interesting glimpse into what it really takes to make super-resolution

tick.

Perhaps it is worth saying a few words about how this book can be used.

As with any edited volume, it is intended to provide a snapshot of the ﬁeld,

which is sure to evolve over time. Yet, I have endeavored to organize the

chapters to be used as a teaching tool as well. Indeed, the ﬁrst four chapters

can easily be incorporated into the latter part of a graduate-level course on

image processing. Other selected chapters of the book can be used to oﬀer

short courses on the subject to a wide audience of engineers and scientists.

The book as a whole can also be used as a text for a semester-long focused

seminar course on the topic, with one or two lectures dedicated to each chapter.

It is hoped that over time the authors may provide supplementary material

for each chapter, including slides, code, or data, which will be archived at the

book Website – so the interested reader is encouraged to revisit the site.

This book is the collective eﬀort of a kind group of friends and colleagues.

I am grateful to each of the authors for giving generously of their time and

contributing to this book. I am also thankful to my students past and present

for their contributions to this topic, and to this book in particular. Speciﬁcally,

I acknowledge (soon to be Dr.) Hiroyuki Takeda for his assistance with myriad

X issues.

It is my hope that this book will help to promote this ﬁeld of endeavor for

many years to come.

Peyman Milanfar

Menlo Park, March 2010

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