超优美的英文句子（精选6篇）
超优美的英文句子 第1篇
1. 寂寞的人总是记住生命中出现的每一个人，正如我总是意犹未尽地想起你!
2. 命里有时终需有命里无时莫强求。
3. 哪里有真爱存在，哪里就有奇迹。
4. 那些刻在椅背后的爱情会不会像水泥地上的花朵开出地老天荒的没有风的森林。
5. 那些以前说着永不分离的人，早已经散落在天涯了。
6. 你给我的一滴泪,我看见你心中所有的海洋。
7. 你笑一次，我就可以高兴好几天;可看你哭一次，我就难过了好几年。
8. 我总是爱蹲下来看地上时光的痕迹，像一行一行蚂蚁穿越我的记忆。
9. 我总是这样凝望那些日升月沉无家可归的忧伤。
10. 幸福的時刻，一半是和你在一起，一半是在夢里;痛苦的時刻，一半是分離，一半是默默地想著你。
11. 既不回头，何必不忘。既然无缘，何须誓言。今日种种，似水无痕。明夕何夕，君已陌路。
12. 心微动奈何情己远，物也非，人也非，事事非，往日不可追。
13. 也许是前世的姻，也许是来生的缘，错在今生相见，徒增一段无果的恩怨。
14. 一年老一年，一日没一日，一秋又一秋，一辈催一辈一聚一离别，一喜一伤悲，一榻一身卧，一生一梦里寻一夥相识，他一会咱一会那一般相知，吹一会唱一会。
15. 忘掉感伤，忘掉为你圆的慌。
超优美的英文句子 第2篇
2.人家说目的地不重要，重要的是旅行的过程嘛。
3.人生就像旅行，不在乎旅途的终点，在乎沿途的风景。
4.旅行的意义在于找到自己，而非浏览他人。
5.两年，很短，让我们看不出自己已经长大;两年，很长，让我们看着她绽放光芒。那个唱歌去旅行的女孩，在喜欢她的人心中，游历到现在，美妙的声音穿透一束束绿光锁进心的镜头
6.没有陪伴与目的的旅行，竟然并不荒芜。
7.其实一部电影的诞生就像一场旅行，主角遇到很多人，由一个人的视角转到无数人的视角，又由无数人的视角看到更多东西。
8.要么旅行，要么读书，身体和灵魂，必须有一个在路上。人生，就是一段路，或长或短，或弯或直。要么，让身体硬朗地行走，要么，让灵魂高贵地云游。你能触及的，无论是身体还是灵魂，都是一种阅历。旅行，亲历各种不同的风景;读书，领悟各种不同的人生。只要在路上，光阴就不是虚掷，幸福就会光临。
9.人生是一场渐行渐远的跋涉，每一天都是这场跋涉中沿途路上的风景，不同的风景方能成就精彩的人生，一尘不变带来的只能是一场无聊的旅行和一个不知终点的迷路。
超优美的英文句子 第3篇
High-resolution(HR)images are valuable in many fields,such as in remote sensing,medical image diagnosis high-resolution video.However,due to the physical limitation of image devices,sometimes it takes high cost to obtain high-resolution images.Super-resolution(SR)method is an effective technology to obtain high-resolution image.The methods mainly include multi-frame reconstruction method[1,2,3]and learning-based method[4,5].The fundamental of reconstruction-based SR method is to extract high-frequency information from series of low-resolution images using signal processing technologies.It assumes the Low-resolution(LR)images are wrapped,blurred and down-sampled from the corresponding HR image,and HR image can be reconstructed from the sequence of LR images by modeling the image degradation process.However,it is hard to define some of the parameters of the model,especially the sub-pixel registration parameter among LR images.So this kind of method has its limitation in resolution enhancement.
Learning-based SR method has received much more attention since it was firstly presented by Freeman et al[4].Its main idea is to predict lost HR details by LR observations using a training set.In the field of learning-based SR method for face image,Baker et al[1]firstly presented the idea of face hallucination.Liu et al[6]presented a similar idea,which unified the global structure and local feature information of face image.Hertzmann et al[7]and Efros et al[8]presented a local feature transform method called as Image Analogies(IA),which is also used for learning-based SR.Chang et al[9]defined small image patches in both the low-and high-resolution images as two distinct feature spaces which are formed manifolds with similar local geometry.The model is inspired by manifold learning methods,particularly Locally Linear Embedding(LLE).
Learning-based SR algorithms usually take two steps:1)search K similar feature examples from training set;2)optimization estimation based on the K examples.The simplest method is K Nearest Neighbor(K-NN)algorithm[4].But these algorithms depend on the quality of the K candidates.That limits the freedom of the model and wastes prior information of other patches in the training set.To overcome these problems,Yang et al[10]presented a learning-based algorithm based on sparse coding(SC).The method can effectively build sparse association between high-and low-resolution image patches and get excellent results.The dictionary of their research is self-adaptive.It is usually expressed as an explicit matrix.The limitations of the dictionary include:1)i is not regular;2)it is in lack of efficiency;3)complexity constraints limit its size.To break through these limitations,Rubinstein et al[11]presented a parameter model called sparse dictionary(SD)to balance efficiency and adaptivity,which decomposed each atom in the dictionary by a basis dictionary.Compared with SR based on overcomplete dictionary,this model has a simpler and higher adaptive structure.
In this paper,we combine the ideas of the SR algorithm via sparse coding[10]and sparse dictionary[11],and present a novel SR method based on sparse dictionary.This method mainly takes three steps:1)build sparse dictionaries Ah and Al using lots of examples,which include high-and low-resolution image patches;2)calculate sparse representation of input image patches based on Al;3)estimate HR image patches via the sparse representation and Ah.Experiments with natural images show our method outperforms several other learning-based algorithms.The major contributions in this paper as follows:
This paper presents an improved super-resolution framework based on the sparse dictionary,which improves not only adaptivity and flexibility,but also its regularity and efficiency.
Compared with Yang’s sparse coding SR method,we choose the high-frequency component of the HR image patch as its feature for dictionary training,which builds the sparse association between LR image patches and HR ones with better efficiency and less training examples.
1 Sparse Representation
The key idea of sparse coding assumes that natural signals can be compactly expressed,or represented efficiently as a linear combination of atom signals,where only few coefficients are non-zero.The sparse coding of an observed signal x can be expressed as[11,12]:
Where the coefficientαis the sparse representation of x,εis the error tolerance and D=[d1,d2,…,dL]∈RN×L(N
We can also use a regularization parameterλ>0 to balance the minimal error and sparsity:
The fundamental problem of sparse representation is the selection of dictionary.There are generally two kinds of methods:analytic-based and synthetic-based(learning-based)[11].The dictionary of analytic-based model is also called implicit dictionary,which mainly includes Wavelet,Contourlet,Curvelet,etc.This kind of dictionary is fixed structured with a fast numerical implementation,but certainly in lack of adaptivity.The dictionary of learning-based is inferred by machine learning techniques from some examples with flexible structure and highly adaptability.It is typically called learned dictionary,which can get sophisticated representation and fine performance.However,the model generates an unstructured dictionary,which lost regularity and effectiveness Sparse dictionary[11]combines advantage of both aforementioned methods,where the dictionary atoms are sparsely represented over a known implicit base dictionary:D=ΦA.The new parameter model leads to a compact and flexible sparse dictionary representation which is both adaptive and efficient.More advantages of the new method include improving stability and accelerating sparse coding.
2 SR via Sparse Dictionary
This paper unifies the sparse dictionary model[11]and super-resolution idea via sparse coding[10],and presents an improved SR algorithm based on sparse dictionary.The method efficiently builds sparse association between high-frequency(HF)components of HR image patches and LR image feature patches,and defines the association as a prior knowledge to guide super-resolution reconstruction based on sparse dictionary.
2.1 Sparse Dictionary Training
Replacing D in(2)withΦA,the sparse representation problem of signals X over sparse dictionary can be defined as[10,11]:
In this case,Z is sparse representation of X,Φis implicit dictionary of D(overcomplete DCT is applied in this paper),A is sparse dictionary,which is also the coefficient of D,s and p are the levels of sparsity,the normalization constraint is commonly added for convenience.
,which denotes the training set that contains HR image feature patches Xh and LR image feature patches Yl,where,.Each example pair(xi,yi)is expressed as a column vector,xi denotes the HF component of HR image patch,yi is LR image feature patch(how to extract image feature is shown in Section 2.3).The goal of the model is to estimate sparse dictionaries using P,and define high-and low-resolution image feature patches in a unified frame,so that they share the same sparse representation.The problem can be expressed as:
Where Ah and Al are the sparse dictionaries of the HR and LR image feature patches,respectively.N and M are the dimensions of the HR and LR image feature patches in vector form,respectively.The goal is to minimize the impact of scale problem(5)can be rewritten
We apply sparse K-SVD algorithm[11]to achieve(6).Sparse dictionary learning algorithm is shown:
1)Task:estimate sparse dictionary Ah and Al;
2)Input:training set:P,implicit dictionary:Φ,sparse tolerance of A:p,sparse tolerance of Z:t,iteration number:k;
3)Init:Ah(0),Al(0);
4)for each example xic in P
Sparse coding step:apply basis pursuit algorithm to the problem:
Atoms updating step:based on Lemma 1 in[8],updating each aj in A,and zj in Z.
2.2 SR via Sparse Dictionary
Given a LR image feature patch y,based on the sparse dictionary Al,we estimate the sparse representation a.Then,we can get the super-resolution image feature patch from a and sparse dictionary Ah.According to(1),the sparse representation problem of y can be defined as[10]:
Although(7)is a NP-hard problem,Donoha[12]has proved that as long as the coefficientαis sufficiently sparse,the problem can be solved by minimizing the l1-norm instead:minΦy A.However,(7)is achieved individually for each local LR image feature patch,and it does not consider the compatibility between adjacent HR feature patches.Similar to Freeman’s method[4],the patches are processed from left to right and top to bottom.The optimization problem can be rewritten:
Where the matrix R is applied to extract the region of the overlap between current target HR feature patch and previously reconstructed HR image,and w denotes the values of the previously reconstructed image feature patch in the overlap.According to(3),the optimization problem can be simplified as:
Whereβcontrols the tradeoff between LR input feature patch and overlap region of HR feature patch.To achieve(9),we can use basis pursuit algorithm[13]to estimate the most optimization result a*.Then the high-frequency component is got:x*=Φh⋅Ah⋅a*.Finally,we linearly synthesize x*and the upsampled image of y to get the initial super-resolution result X0.
According to Yang’s method[10],to enforce global reconstruction constraint,we project X0 onto the solution space of Y=D⋅H⋅X,computing
Where D is a downsampling operator,and H represents a blurring filter.This optimization problem can be solved using the back-projection(BP)method.The BP result X*is taken as our final super-resolution estimate of the input LR image.Details of SR algorithm via sparse dictionary are shown:
1)Task:estimate HR image X*;
2)Input:Al,Ah,implicit dictionaryΦ,input LR image Y0;
3)Init:caculate feature image Y from Y0;
4)for each 3×3 patch y in Y,from left to right,and top to bottom,keeping one pixel overlap region.
Estimate optimization value a*in(9).
Calculate the final optimization value x*=ΦhAha*
5)Linearly synthesize the upsampled image of y and x*,get the initial super-resolution estimation X0.
6)Calculate the final estimation X*in(10)to enforce global reconstruction constraint.
2.3 Patch Feature Representation
Since people are more sensitive to the HF content of the image for a perceptual viewpoint,the image patch feature is usually selected as HF component during SR reconstruction.In the literature,the method that extracts high-frequency feature mainly includes Laplace transform[4],Gaussian derivative filters[14],first-and second-order gradients information[9,10].During sparse dictionary coding,the training examples consist of feature patches rather than raw image patches.We select the features of LR image patch that is same with the methods of Chang et al[9]and Yang et al[10].The four filters used to extract the derivatives are:
We also use the HF components of HR image patches as the feature,which is same with the method of Freeman et al[4].Then,each training example is defined as a column vector,which contains HF component of HR image patch and four gradient features of LR image patch.Fig.1 shows the composition of training example.
3 Experiments
We use the same training images in[10],and downsize them to 1/3 of their original size.Then we use severa typical learning-based methods,i.e.,K-nearest neighbor(K-NN)[4],LLE method[9],and Yang’s SC method[10],as well as our proposed sparse dictionary based method to perform SR by a factor of 3 to up-scale these downsampled input images to their original size.3×3 LR image patches are adopted,with one pixel overlap region with adjacent patches,and the corresponding 9×9 HR image patches have three pixels overlap.According to Yang’s method[10],we also extract gradient features from the upsampled version(6×6)of the LR image by a factor of 2 instead of 3×3 LR image patches.
3.1 Results of Super-resolution
We take 50 000 HR and LR image patch pairs randomly extracted from the training images as training examples for sparse dictionary learning.In our experiments,the size of sparse dictionary is fixed as 1 024,which is a trade-off between SR quality and computational efficiency.Setting the parameterλ=0.01 andβ=1 in(9).Fig.2shows the results of our method with those of K-NN,LLE and SC method on an image of the Face.Visually,the result from K-NN has some jagged effects.LLE generates blur effects.Our method and SC have better visua effect.We compare these SR methods quantitatively in terms of their PSNR of several images(Face and Lena are color images).As the results we can see from Table 1,the SR quality of our method(SD)is improved in the terms of PSNR except the image Cameraman,which further demonstrates our method could get better SR results.
3.2 Effects of Examples Number
In the section,experiments with the image Face show the effects of the number of training examples.The results are shown in Fig.3.We select randomly 50 000,30 000,10 000,5 000 and 2 000 number of examples to train the sparse dictionary,and perform SR by a factor of 3 using our method and Yang’s algorithm(SC).As is shown in Fig.3,PSNR of our method is higher than SC algorithm with the same training examples.When the number of training examples is less than 5 000,the PSNR of the two methods falls sharply.To gain better quality the number of examples should be no less than 30 000.
3.3 Effects of the Dictionary Size
We use 30 000 examples to train the sparse dictionary.The image Face is selected for SR experiment.The dictionary size is chosen as 256,512,1 024,and 2 048,respectively.The SR results by a factor of 3 using our method and SC algorithm are shown in Fig.4.Under the conditions of the same dictionary size,our method performs well than SC algorithm.Theoretically,the larger the dictionary size is,the better SR results gained,bu with a higher computational cost.As is shown in Fig.4,when the dictionary size increases,the PSNR tends to rise gently.In practice,the dictionary size is chosen as 1 024 for a trade-off between SR quality and computation efficiency.
4 Conclusion
让状语点亮你的英文句子 第4篇
首先，如果是形容词（短语）充当状语，那么它在句子中所起到的作用有两种情况：一是说明句子的谓语动词发生时主语所处的状态，一是说明句子谓语动词发生的原因。
[例1]He stood there， still， except that his lips moved slightly. 他站在那里，一动也没动，只有嘴唇在轻微地抖动。（still说明主语stood时所处的状态）
[例2]Hungry， they walked into the restaurant. 由于饥饿，他们走进了一家餐馆。（hungry 说明了他们走进餐馆的原因）
写作点拨：巧用形容词（短语）作状语，可把句子由复合句变成高级的简单句。
[例3] 觉得筋疲力尽，我爬上床，很快就睡着了。
Exhausted（=As I was exhausted），I slid into bed and fell fast asleep.
[例4]我熟悉本地的风土人情和文化，并能为客人做相关的介绍。
Familiar with our local custom and culture（=As Im familiar with...），I will be able to make any related introduction to the visitors.
二、简单句也灵动，副词/介词短语显神通
副词/介词（短语）在句子中作状语时，通常表示时间、地点、频度、程度、方式等，其位置可在句首、句中、句末。但当多个状语连用时，一般遵循先单词、后短语；先地点、后时间；先小概念、后大概念的顺序。在英语写作中，灵活地将副词与介词短语相互结合起来，不但可使一个简单句结构更加巧妙，也是学生不俗表达能力的体现。
[例5]He went out of the room at a quarter to 23：00 last night and then disappeared into the dark. 他昨夜22点45分从房间里出来，然后消失在黑夜之中。
[例6]Well gather at the Students Club at 8 pm. this Friday， after the evening classes. 我们将在周五晚上8点，晚自习后在学生俱乐部集合。（2012 大纲卷）
[例7]Id like to invite you to join us for a visit to the nearby nursing home next Saturday for the Double Ninth Festival. 我们想邀请你下星期六和我们一起去附近的敬老院陪老人们过重阳节。（2015 课标卷Ⅱ）
写作点拨一：表示频度的副词放在be动词、情态动词或助动词之后。
[例8]He is always late for school. 他上课总迟到。
[例9]I have never seen such a good person. 我从未见过这么好的人。
写作点拨二：借助评注性状语使语言更简练。
[例10]It is important that many people in our city have come to realize the importance of helping the disabled.→More importantly， many people in our city have come to realize the importance of helping the disabled. 重要的是，我们城市中的很多人已经意识到帮助残疾人的重要性了。
相类似的评注性状语还有：obviously， clearly， undoubtedly， surprisingly， unbelievably， fortunately， unfortunately， luckily， unluckily等等。
三、从属连词架桥梁，句间逻辑更顺畅
在复合句中起状语作用的从句叫状语从句。状语从句由从属连词引导，按其意义可分为时间、地点、原因、目的、结果、条件、比较、方式、让步等。
[例11]只要有趣有益，任何相关的东西你都可以写。（2015 课标卷Ⅰ）
You can write anything relevant so/as long as it is interesting and informative.
[例12]它（Global Mirror 周报）兼顾国内外新闻，以使我能了解到一周发生的一切重要事件。（2011 大纲卷）
It covers both national and international news so that I can learn about all important things that have happened during the week.
[例13]我从小喜爱大熊猫。（2008 全国卷Ⅰ）
I have been a panda lover since I was a child.
[例14]若有任何问题，请致电44876655。（2010全国卷Ⅰ）
Please call me at 44876655， if you have any questions.
写作点拨：恰当运用高级连词可以提高句子档次。
[例15]他一到我们就点亮蜡烛，一同唱响生日歌。（2012大纲卷）
When he comes， well light the candles and sing“Happy Birthday”together for him.
→Well light the candles and sing “Happy Birthday”together for him as soon as / the minute / immediately， directly / instantly he comes.
四、活用非谓语，句式百变、更简练
1. 不定式短语作状语
不定式短语作状语通常表示目的、结果（出乎意料的），但逻辑主语一般是句子的主语。
[例16]听到枪声，他迅速跑到楼下，结果却发现她躺在地板上，失去了知觉。
On hearing the shot， he rushed downstairs， only to find her lying unconscious on the floor.
[例17]我，代表学生会，写这封信，倡议所有的高三学生在高考后捐出自己的参考资料，如旧参考书，笔记本，微型字典等等。
On behalf of the Students Union， I am writing to call on / appeal to all Senior 3 students to donate your reference materials such as the old reference books， notebooks， pocket dictionaries and so on， after the college entrance examination.
2. 现在分词和过去分词短语作状语
分词在句中充当状语时一般表示原因、时间、方式、伴随、结果、让步或条件等，相当于相应的状语从句。
写作点拨：借助分词作状语，一方面可使你文章的句式呈现多样化，一方面也使句子更简练；但是要注意其逻辑主语必须是主句中的主语（意料之中的结果状语例外）。
[例18]听了这个消息，她的眼泪都流出来了。
→When she heard the news， tears came to her eyes.
→Hearing the news， she burst into tears.
[例19]不幸的是，他的父亲离世，这使得他的家庭更加贫困。
→Unfortunately， his father died， leaving his family even worse off.（人教版Module 4 Unit 3中课文中的句子）
[例20]Tracy来电留言说，因有要事，明早咖啡屋见面取消。（2009全国卷Ⅰ）
→Tracy called， saying that she couldnt meet you at Bolton Coffee tomorrow morning as she has something important to attend to.
总而言之，写作是考生综合能力的体现，需要长期的点滴积累（尤其是对课本中重点单词、短语和句型的掌握等等）和在运用中的不断修正，让我们继续努力吧。
超有意境的文艺优美句子 第5篇
2. 梦碎了一地，我还在原地等你。
3. 回忆始终抹不去，你是否和我一样还在念着曾经。
4. 永远记得你微笑的眼睛，你的答案永远缺少一个回忆。
5. 错了也不要后悔，后悔就意味真的错了、
6. 或许，有些人有些事，该放弃的就应该放弃了。
7. 请让我独自待在一个没有名字的陌生地方，忘记所有关于你的一切
8. 没有人疼爱的人，在美也会憔悴。
9. 我被画上了×，就代表我不再重要。
10. 爱情的世界，始终没有我的容身之处。
11. 深呼吸，贪婪的搜集有你存在的空气。
12. 都说有点距离才会有美感，而我们因为距离出了小三。
13. 想的太多，得到的太多，失去的也太多。
14. 海鸥掠起我记忆涟漪，让回忆画面正在上演。
15. 即使是被捧在手心，却还是不确定心里有没有你。
16. 风夹着雨丝吹在我身上，那种冰凉的折磨、习惯后就会变成享受
17. 我为你流下的眼泪，痛彻心扉谁来安慰。
18. 太美丽的东西我们往往触碰不到，彩虹就是一个榜样
19. 怀念也回不到从前，向往也不知该如何向前。
20. 永远真的太久了，对不起，我等不到那一天。
21. 我想忍住悲伤，停住眼泪，却制止不了那蔓延的情绪。
22. 爱情就是一个玩笑，被玩了还要接着笑
23. 这夜太美，勾起了不该被释放的回忆
24. 看穿你只是长的好看，可惜橱窗里不存在真爱。
咖啡的优美英文句子 第6篇
有咖啡才有生活
2. A yawn is a silent scream for coffee.
打哈欠是对咖啡无声的诉求
3.Given enought coffee, I could rule the world.
给我足够的咖啡，我就可以统治世界
4. But first coffee.
万事咖啡先
5. Rise and grind.
现磨是王道
6. Coffee is always good idea.
来杯咖啡总归是一个好想法
7. Coffee in the morning makes me happy.
清晨来一杯,快乐一整天
8. All you need is coffee.
你只要有咖啡就足够了
9. Java now, sleep later.
马上喝咖啡，让睡眠见鬼去吧
10. Give me coffee to change the thing I can and wine to accept those that I cannot.