超唯美英文的爱情语录

超唯美英文的爱情语录（精选6篇）

超唯美英文的爱情语录 第1篇

Distance makes the hearts grow fonder.

距离使两颗心靠得更近。

I need him like I need the air to breathe.

我需要他，正如我需要呼吸空气。

If equal affection cannot be, let the more loving be me.

如果没有相等的爱，那就让我爱多一些吧。

When you need to tell, I am here; When you need a warm hug, I will be here; When you need someone to wipe your tears of sadness, I will here.

当你需要倾诉，我就在这里;当你需要一个温暖的拥抱，我就在这里;当你需要有人为你擦去伤心的泪水，我就在这里。

Dont forget the things you once you owned. Treasure the things you cant get. Dont give up the things that belong to you and keep those lost things in memory.

曾经拥有的，不要忘记。不能得到的，更要珍惜。属于自己的，不要放弃。已经失去的，留作回忆。

It takes being away from someone for a while, to realize how much you really need them in your life.

有的时候，你需要离开某人一段时间，才会发现自己有多需要他。

Staring today, I am going to do myself a favour. To forget about the burdens. To forget about the pains. To forget about the hurts.

从今开始，我要帮自己一个忙。卸下负担。忘却疼痛、抚平创伤。

Do you understand the feeling of missing someone? It is just like that you will spend a long hard time to turn the ice-cold water you have drunk into tears.

你知道思念一个人的滋味吗，就像喝了一大杯冰水，然后用很长很长的时间流成热泪。

Love is a vine that grows into our hearts.

爱是长在我们心里的藤蔓。

If I know what love is, it is because of you.

因为你，我懂得了爱。

Love is the greatest refreshment in life.

爱情是生活最好的提神剂。

Love never dies.

爱情永不死。

The darkness is no darkness with thee.

有了你，黑暗不再是黑暗。

Sometimes the right person for you was there all along. You just didnt see it because the wrong one was blocking the sight.

有时候，那个人就在你身边。只是你不知道他的存在，因为错误的人挡住了你的视线。

If you leave me, please dont comfort me because each sewing has to meet stinging pain.

离开我就别安慰我，要知道每一次缝补也会遭遇穿刺的痛。

Memories, beautiful very hurt, memories, memories of the past but can not go back.

回忆，很美，却很伤;回忆，只是回不到过去的记忆。

If we can only encounter each other rather than stay with each other, then I wish we had never encountered.

如果只是遇见，不能停留，不如不遇见。

If two people are meant to be together, eventually they will find their way back.

如果两个人注定在一起，最终他们总会找到重温旧梦的路。

The best things in life are unseen，thats why we close our eyes when we kicry and dream.

生命中最美好的都是看不见的，这就是为什么我们会在接吻，哭泣，许愿的时候闭上眼睛

Where there is great love, there are always miracles.

哪里有真爱存在，哪里就有奇迹。

Love is like a butterfly. It goes where it pleases and it pleases where it goes.

爱情就像一只蝴蝶，它喜欢飞到哪里，就把欢乐带到哪里。

If I had a single flower for every time I think about you, I could walk forever in my garden.

假如每次想起你我都会得到一朵鲜花，那么我将永远在花丛中徜徉。

Within you I lose myself, without you I find myself wanting to be lost again.

有了你，我迷失了自我。失去你，我多么希望自己再度迷失。

At the touch of love everyone becomes a poet.

每一个沐浴在爱河中的人都是诗人。

Look into my eyes - you will see what you mean to me.

看看我的眼睛，你会发现你对我而言意味着什么。

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超唯美英文语录

超唯美英文的爱情语录 第2篇

2. 一生之中一定会遇到某个人，他打破你的原则，成为你的例外，成就你全世界的幸福。

3. 都说：爱比不爱更寂寞，我说：爱比不爱更悲伤。就像正午的阳光，捧起的是满手的温暖，握起来的却只剩下余温。或许每个人心中都有架尘封的钢琴在回忆里，任凭我只是你的一个插曲。

4. 我叫你亲爱的，是因为我爱你;你叫我宝贝，是因为你宝贝我。我们一定要去同一个地方，因为我们要在一起幸福的生活。我们的家可以不够大，但是一定要有温暖的感觉。因为你笨手笨脚，所以家务的事交给我。你是幸福的，我就是快乐的，为你付出再多我也值得

5. 在爱的世界里，没有谁对不起谁，只有谁不懂得珍惜谁。人总是珍惜未得到的，而遗忘了所拥有的。走得最急的，都是最美的风景;伤得最深的，也总是那些最真的感情。

6. 很久以前，我们无数次的擦肩而过，从今以后，我再不会错过与你牵手的机会，因为爱你，所以愿意，愿意去用心，愿意去珍惜，由你存在我的生命里。爱上你，是我今生的美。

7. 年轻的时候，你爱上一个人，请一定要温柔地对他，不管你们相爱的时间长短，若能始终温柔地相待，所有的时刻将是一种无瑕的美丽。若不得不分离，要好好地说再见，要心存感激，感谢他给了你一份记忆。长大后蓦然回首，你才会知道，没有怨恨的青春才会了无遗憾，如山冈上那轮静美高洁的满月。

8. 我不想去打扰你，只想好好的祝福你，毕竟爱情不是占有，而是真心的去祈祷，祝福心爱的那个人永远幸福，我不再联系你了，因为你也没有联系我，我相信现在的你过得很好，我从来都是这么相信你，无数个夜晚我都在想这辈子，可能真的就和你在一起了。

9. 爱上一个人并不难，难的是你看清楚了一个人却仍然爱着他。两个人相处久了，难免就会抱怨一句“你变了”“你也变了”。你想过吗?也许我们并没有改变，我们只是越来越接近真实的自己。

10. 爱一个人最重要的也许不是山盟海誓和甜言蜜语，生活中的一些琐事，更能体现他对你的用情，那才是爱的密码。

11. 爱，也是人存活世间的证明。我们经常看到，在一起的人未必不相爱，相爱的人却被迫分开，这是人生无常，也怪很多人自己没有努力。爱应该是一种忠诚和无私的付出，一种勇敢而无畏的表达，这是我们作为人的权利，千万不能只当儿戏。

12. 世界上最美妙的感觉是 --当你拥抱一个你爱的人，他竟然把你抱得更紧。

13. 有人说，爱的反面不是恨，而是淡漠。淡漠，意味着心里不再有对方的位置，而不再想起。没有余恨，没有深情，更没有力气和心思再做哪怕多一点的纠缠，所有剩下的，都是无谓!

14. 在我一生最奢侈的事，就是在途中与你相遇，然后相濡以沫。

15. 我只是要有个人可以一直一直陪我走下去，不需要很慢，只需要很长。

16. 十年前，你爱我，我逃避不见，十年后，我爱你，你不在身边。人生的错过就是如此。一刹便是永远，追悔也是纪念。在心的修行途中，绝不允许投机，面具必被撕毁，谎言必被揭穿，谁也无法幸免。

17. 只要你肯为我流一滴眼泪，我就可以为而你活下去。

18. 既然爱，为什么不说出口，有些东西失去了，就再也回不来了。

19. 你可以不相信一见钟情，但一定要听从内心的召唤。如果你是单身，看到喜欢的人一定主动及时地追求表白，不要因为明天而错失机会。用具体的行动表达你的爱，平时的冲动浪漫你一定要想到就去做。

20. 现在让你伤心的，将来你一定会忘记。现在让你开心的，将来不一定陪着你。谈恋爱，谈的是现在。而结婚选的是未来。真正会影响到你生活的，不是当前的情绪，而是对方的人品。会谈恋爱的，不一定是好男人。而不会谈恋爱的，却可能是好老公。选男人，要看人心，更要看人品。

21. 爱情，有时候，是一件令人沉沦的事情，所有的理智和决心，不过是可笑的自我安慰的说话。爱情从来都是一种束缚，追求爱情并不等于追求自由。因为爱一个人，明知会失去自由，也甘愿如此。

22. 因为爱情的缘故，两个陌生人可以突然熟络到睡在同一张床上。然而，相同的两个人，在分手时却说，我觉得你越来越陌生。爱情正是一个将一对陌生人变成了情侣，又将一对情侣变成了陌生人的游戏。

23. 那些你以为不可失去的人，原来并非不可失去。你流干了眼泪，自有另一个人逗你欢笑，然后发现不爱你的人，根本不值得你为之伤心，回首之前，何尝不是一个喜剧?情尽时，自有另一番新境界。

24. 是否你已经忘记了怎么来爱还是我已经改变了爱你的想法。

25. 失踪的爱人，其实从来不曾真正地属于你，不必可惜。

超唯美英文的爱情语录 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]∈RNL(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 33 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.33 LR image patches are adopted,with one pixel overlap region with adjacent patches,and the corresponding 99 HR image patches have three pixels overlap.According to Yang’s method[10],we also extract gradient features from the upsampled version(66)of the LR image by a factor of 2 instead of 33 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

奥迪TTS 唯美超跑的运动感 第4篇

外观：亮丽的风景线

看见TTS，人们一定会不带任何迟疑的爱上它。它将奥迪家族坚持的低调路线一抛脑后，尽情的去收揽路人的眼光，它的目的就是成为路上的焦点。奥迪TTS在外形方面做了改变，全新的黑色高亮进气格栅设计由原来的獠牙状改变为五辐横条；前大灯上加装了奥迪家族式的LED日间行车灯，与氙气大灯荣威一体的设计整体性更强；雾灯加装了镀铬装饰环，为整体增加了一些豪华感。

作为一款非常拉风的车型，其优雅的coupe造型也是为外形加分的因素。如果说颜色是第一印象，那优雅的曲线设计才是让你痴迷一款车型外形设计的真正原因。顺着圆滑的线条来到车尾，浑圆的尾部设计依然是TT车系的指标精神。

在挡风玻璃下缘，TTS同样将扰流尾翼隐藏在钣件之内；在车速超过120km/h时，扰流尾翼便会自动升起，来增加高速下的下压力道；而车速降至80km/h以下时，尾翼则会自动收纳隐藏。当然，车主也能自排档座后方的按键，手动操作尾翼升降高度。你们是不是对两双“大脚”感觉很熟悉？没错，这款车型的轮毂和去年的那款完全相同，规格为245/40R18的马牌轮胎刚劲十足。它的尾部依旧搭载了那个漂亮的尾翼，除了手动开启，当车速超过120km/h的时候它也可自动升起。

内饰：炫！炫！炫！

坐进TTS的瞬间，便可感受坐姿与传统轿车有相当不同。相当低矮的乘坐高度，让乘驾TTS更多一分性能意味，也降低整体的重心高度。TTS的座舱设计仍保持车系基调，圆形造型成为车室设计的基础元素；而大量运用金属饰板点缀，TTS也展现出符合年轻口味的科技形象。

TTS中控台采驾驶导向的设计，整体略为朝向驾驶座方向。在三组圆形出风口下方，音响系统及空调系统依序排列。音响系统TTS仅标配传统的六片CD音响，而Bose扬声器列为标配项目，以12具扬声器营造优异的车室聆听效果。设计师以三组圆形旋钮，作为空调系统的主要操作界面。清楚易懂的图形说明，让乘员得以直觉式地操作温度设定、风量及吹拂位置；操作手感及整体设计质感上，TTS也展现高度的工艺水平。

TTS采用清晰易辨的双筒式仪表板，扭开电门后，各组警示灯组亮起的同时，时速及转速表两组指针会扫至表底进行自我检测，红线区划在7,000rpm，时速表底也直指300km/h。

动力：全面进化

掀开发动机盖，TTS搭载的2.0升TFSI缸内直喷涡轮增压发动机，名称看似熟悉；广泛用于Volkswagen集团的2.0 TFSI发动机，最广为人知的应用乃最大马力200匹的五代Golf GTI，然而名称虽同，里子内却是另一番惊人声势。

发动机四组尾管似已按捺不住久候，想要急声高亢一番。饱足的动力搭配AFS铝合金车体结构，TTS拥有优异的马力重量比；而此一优势也展现在加速性能上：饱满的扭力输出，让起步加速显得快捷无比。发动机转速可即刻进入扭力高原，让车身加速力道达到最大化；而加速力道也得以延续至5,000余转，让驾驶用尽每一分气力后，再按下换档拨片持续追高车速。

编辑点评：在我们对其进行实拍的过程中，这款车型赢得了不少人的驻足围观，看来绝大多数人还是很难拒绝炫丽的它，它不再低调，从里到外都十足的令人雀跃，成为我们视野里一道独特的风景线！

爱情超唯美语录 第5篇

2、我只希望我所爱的女人，平凡而孱弱，不必事事自己挡在前头，任何事情发生，都可以有人替她遮挡风雨，尽力照顾她，疼爱她。我只希望你可以从容幸福，安宁地过完下半生。我只是要你幸福。

3、遭遇爱情，有人愿意做一个傻瓜，因为相信信任最重要，没有必要为了捕风捉影的事情，让自己的爱情不堪回首;而有些人，希望自己做一个明白人，不希望自己在面对爱情时，是自欺欺人。

4、其实对待爱情，就应该如同照顾鱼缸中的热带鱼，必须常常换水，以保新鲜，这样五颜六色的热带鱼才能自在顺心地摇摆出绚烂的生命力。

5、在爱情里，我们长大，我们也老去。我们牵挂，我们思念，我们却明白了有多少牵挂，也就有多少牵绊。我们学会了爱，我们却也看透了爱。我们变聪明了，却也因为聪明而看穿了谎言。

6、不要让爱成为后悔，因为选择去爱了，就不要因为一些风雨而放弃。路总是坎坷的，平坦的路会失去人生的意义。同样的，爱也是如此，因为痛才要更加珍惜，不要放他离去。

7、不知有多少人懂得，在爱情的两端，一端是索取，一端是付出。只有索取与付出两厢平衡，爱情才能滋长。只是，索取和接受总是比付出来得容易，于是我们总是光顾着索取，忘记了也要付出。

8、在爱情里保持一种傻傻的状态，就是对对方的信任，太精明就是不信任。”信任是互相的，你对我无比信任，我也会对你同样信任，而这样的信任正是牢固爱情的基石。

9、在人生中有许多际遇，那些际遇让我们一生都难以忘记，沉淀在心里成为永久的痕迹。其中最难以忘记的是与你的相遇，在偶然与必然的缝隙间交织，交织成不老的不能忘怀的印记。

10、爱情不是学问，不用学习，若果爱一个人，发自内心，难以遮掩，自然而然以他为重，这是种本能，不费吹灰之力。

11、吵吵闹闹的相爱，亲亲热热的怨恨，一切的沉重的轻浮，严肃的狂妄，整齐的混乱，铅铸的羽毛，寒冷的火焰，憔悴的健康，永远觉醒的睡眠，否定的存在!我感到爱情正是这种东西。

12、爱一个人，原是爱到七分就够了，还有三分要留着爱自己。爱太满了，对他而言不是幸福，而是负担。

13、世界上只有两种可以称之为浪漫的情感：一种叫相濡以沫，另一种叫相忘于江湖。我们要做的是争取和最爱的人相濡以沫，和次爱的人相忘于江湖。也许不是不曾心动，不是没有可能，只是有缘无份，情深缘浅，我们爱在不对的时间。

14、能拥有刻骨铭心的爱是一种幸福。爱极恨极都是情到深处，真正爱过恨过的人都会大彻大悟，我会感恩爱我或恨我的人。爱能燃烧自己温暖别人，恨会灼伤别人毁掉自己。爱有如赠人玫瑰，手留余香，而恨却伤了自己也伤了别人。

15、好的爱情有韧性，拉得开，但又扯不断。相爱者互不束缚对方，是他们对爱情有信心的表现。谁也不限制谁，到头来仍然是谁也离不开谁，这才是真爱。

16、爱是不分国界的，就算远在天涯，心灵的距离也不会太遥远。

17、爱情需要一些忧伤和一点戏剧性的结局，那样才更显得朦胧而珍贵。

18、我们懂得幸福的时候，是因为我们懂得了珍惜。

19、得到和失去是一种偶然也是一种必然，也许只有我们不懂。

20、不要过多的去计较得到了些什麽，而应该多问问自己付出了些什麽。守候的本身，便是爱情，不需要任何的结果。

21、当爱情日渐消逝，就像一辆公路上高速飞驰的车子，要停也是停不住的。

22、这个世上没有未完的故事，只有未死的心。

23、我们往往在失去时才明白自己曾经拥有的东西是这麽美的;然而，同样的真理是：当我们能够拥有一样东西时，我们才会明白自己从前失去一些什麽。

24、上帝让我们在情路上首先遇到几个错误的人，然后才找到适合的人，也许是要我们感激他们。

25句超唯美爱情语录。 第6篇

1、当爱情走过沧海桑田，当想念化作担忧，当等待化为牵挂，当我在时光深处读懂了你的忧与愁，欢与乐。这一生，但求，所有的岁月里都与你相伴，所有的幸福都与你分享，所有的快乐都与你缔造。人生若只如初见，何事秋风悲画扇。亲爱的，当我们的爱走到时光的最深处，我回眸浅问一声，来生，我们能否依然永如初遇。

2、当岁月无情的斑驳了三生石上的情缘，我唯有放飞心灵深处相思的云，任她飘荡，覆盖在有你的天空，寄去我浓浓的相思。风，再把一袭思念的暗香，悄然送入你浸满泪花的梦里，轻诉一腔的眷眷思情，荡开你眉宇间的苦涩。隔着无情的轩窗，遥望此时的明月，正渐渐地盈满，想起你用桃红串联起的精彩人生，敷在我如雪凄冷的心上，碎了月下琉璃的影儿，濡湿了梦里余温的衾枕。许，梦里你依然抖落不了满身凄凉的诗词，然，任忧伤的诗词层层结结，叠叠层层铺满在我的心上，一生不减，一世不变。

3、灯火摇曳，照亮面颊上的晶莹泪珠，衬着凄婉的月色，于舒缓而低沉的笔触中述说着哀怜、动人的爱情。我去海边，带回的蓝色沉静，泪水里有盐的结晶。执手相看泪眼，竟无语凝噎，才知道晶莹的泪花并不完全是痛苦的诉说，也许更是一种情到深处的幸福情缘。

4、滴不尽相思血泪抛红豆，开不完春柳春花满画楼。夜雨霖铃，语罢清宵。谛听命运悠扬的钟声从水中泛起。凝眸深处，心波荡漾。人世间的情，纠纠葛葛，牵牵绊绊，钟情怕到相思路。盼长堤，草尽红心。动愁吟，碧落黄泉，两处难寻。

5、冬季漫步走来，微微寒风夹杂在秋风里缓缓掠过我的发梢，有一些冰凉沁透我的心脾，亲爱，你可记得添加衣服呢？最后一次留言你说感冒了，千里之外我能做些什么呢？插上翅膀的鸟没有归宿的飞过钢铁丛林，可你又在哪里？我该在哪里停下疲惫的步伐，感受你的温暖呢？

6、独立雨巷深处，遥望相思，旖旎风光里夹带着丝丝苦涩凄迷，纠结苦痛中包含着缕缕幸福甜蜜。手持一枚受潮的丁香，手书彼此的传奇生命的青焰，云天苍茫中低吟缓行。青碧的湖水，寒光闪烁的繁星，寂寂的紫陌红尘。谁的歌是那无巢可栖的倦鸟。幽香的百合，凋落在哪一夕？时光的苍苔爬上了残垣的心壁。人生如梦的沉静，在风里吻着我的睡靥低吟。丹彩的流云，泪水凝珠，缀成一帘幽梦。

7、花若谢，定是为你凋零，泪若流，定是为你哭泣；月若瘦，定是为你相思，笔若动，定是为你写诗。执笔流年，醉枕墨香，不管落花有意，还是流水无情，我都愿意用最轻最淡的文字，为你写尽我那最重最浓的相思。信手翻开为你写的诗，开头写着一见钟情，结尾却是一往情深。

8、花开的时候，我知道我的掌心盛开的风声，依然是遥远的张望。月光爬上眉睫之后，我们的影子泊在文字里，写意抒情。一泓秋水，明澈了彼此的心境，一缕秋风，吹熟了彼此的风景。彼岸，依然是我们不敢触及一朵盛开的落鸿。

9、红尘无双，音容无颜，堂而惶之的年华在你来过的生命轨迹里，用芳芳的扉页铸读我一生的情感，每一次的翻阅都会情不自禁迷失在思念的海，那么多日夜我卑微的伏下身来，丈量你我的距离究竟还有多远？我的远方是否还有那临幕的守望？为我，细读每一篇疼爱的诗篇？

10、呵，转眼2012年又要过去了，时间磨去等待的记忆，皱纹苍白的印在岁月磨擦而过的脸颊，韶华即将流失殆尽。远远眺望山的另一边的再另一边，连绵起伏不知要延伸到哪里才是你栖息的地方，记得你说：“不要分开太久，太久了我会受不了的。”是呵，那个时候，只会感觉到铺天盖地的幸福滚滚而来，谁曾想太久是多久呢？时光流逝至今，对于你我来说已然成为天涯各方。

11、寒露浸履凉初透，秋风起，纱帘后。夜，如期而至，寂寞，深邃，浓浓的秋意调和着寥落的心情，凌乱的思绪将千疮百孔的心连成串串摇曳的风铃，每一次清脆的碰撞都是一阵蚀骨的疼痛，千般滋味，跌落入双眸，化为缠绕指尖的流沙，细细的体会。

12、海天茫茫，拉不开恩怨纠葛，扯不断缠绵悱恻，阅不尽人生沧桑，解不完世间风流。我能听到的，唯有我的心，在月升月落时，伴随着凝露的花儿一起绽放，一起凋零。你是否也在临窗思慕，亦如我隔着千山万水的牵念？

13、滚滚红尘浪滔天，遥望中的颠沛流离，我唱一曲忘我的情歌。“有情芍药含春泪，无力蔷薇卧晓枝。”那木栅短篱，一簇花香。那情，会在文字里温柔地潺湲，情缘漫过，爱在其中。细细香色的落花，优雅地枕着水波，恬淡的卧姿，作陪逝水而去的素年锦时。就让一切的一切穿越生命的沧海，我会是你永远隔河相望，无舟可渡的彼岸玫瑰花。

14、古风诗韵中，梧桐这一意象蕴含着丰富的爱情含义，文人骚客总喜欢把梧桐和爱情联系在一起，书写着苍凉多姿的恋歌。

15、爱与情则是两个概念，爱是广博无边的，爱是给以生存的理由；爱是一种无私的奉献精神；是血让肉鲜活的一种自然而然的表现。就如你的肉被割掉一块，血流可能不少，但血还得慢慢让你的伤口补合。如果血流干了，肉又能帮助血做什么呢？因此我本人认为，爱就是让人类得以生存的鲜红血液。它是默默推动延续人类生命的伟大母亲。

16、爱与情既是独立，又是不可分割的。是天然促成一体的，没有爱情就没有完美的婚姻。没有婚姻就不会有新的生命旦生。所以，它是人类有史以来发展的必然先驱。当然也存在没有爱情就产生婚姻的，并且婚后幸福的还不少。这可能得追述到从前的例子很多。也就是我们的父辈以上当中很多是媒妁之言，婚后才建立感情的，而往往他们的这种婚姻却能一直到老，幸福到终。

17、爱一个人就是希望他能够幸福，就是要他一定要幸福，把爱着的人放在心中最柔软的地方，寂然欢喜。亲爱的，你曾说过，今生与我心心相携不离不弃。经年的遇见，你用浪漫的柔情暖一场相逢。那么再遥远的距离，也无法阻挡我对你的思念与牵挂，再寂寞的等待也是一种温暖的牵念。浮世繁华中能够与你邂逅是一种美丽，错落红尘中能够与你心灵相约是一种幸福。

18、大家都知道梦终是要醒的，早晚都一样，你选择了先醒，他宁愿把梦沉迷。没有对错，这只是一种逃离于时光、价值之外的情感，流年宿命中的一种寄生方式。他就在这里，把心灵安置在此，只盼某一日，你踏着熟悉的步伐走进他的家园。

19、错过，就是悲哀。站在深秋的冷风中，我悲叹，半生情缘终难相守。为何相爱的人那么难相处？为何相爱的心那么受伤？为何相爱的故事那么容易改写？

20、初冬的脚步，从遥远的北国翩翩而至，一袭的寒流铺天盖地而来，季节的更替完成了它欣喜或悲壮的过程，给下一个重生的季节让路，秋走了，它带着附予生命的含义，义无反顾的在离别的季节里坦然离开，把不舍和眷恋留绐金黄的大地，从此一别无期，在这一季的相遇里铭刻着永怛的苏醒，冬来了，它温柔而又凛烈，昼夜之间的温存，肆无忌惮的把凄风冷雨演绎，在最后的年末里上演一场聚散的悲伤。

21、尘世间，有些所爱总是无可奈何，可又情不自禁；有些所为已经知道多余，却仍坚持到底。望天涯，月明知要缺，却还是要圆；临流水，花明知要谢，却还是要开；念伊人，心明知要痛，却还是要写。信手执笔，用三分温柔写出你的喜怒哀乐，用七分情愿写尽你的苦辣酸甜，用十分感动写满你的春夏秋冬，然千言万语，写不出的，是你在我梦中的迷离，而词穷意尽，描不出的，是你在我心里的美丽。回眸处，素墨风干，满腹心语，随着花谢花飞，流转天涯，散落成风里的一首首诗。

22、长亭石凳，人走缘散，初上的华灯，为冥暗的天色点了一行行银烛。像是送别的目光对吧，一闪一闪的晶亮，又不见泪水挂睫。笑着送别，是不是也算是一种缘？

23、爱一个人就是希望他能够幸福，就是要他一定要幸福，把爱着的人放在心中最柔软的地方，寂然欢喜。亲爱的，你告诉我愿意珍惜这场相遇。经年的遇见，你用浪漫的柔情暖一场相逢。那么再遥远的距离，也无法阻挡我对你的思念与牵挂，再寂寞的等待也是一种温暖的牵念。浮世繁华中能够与你邂逅是一种美丽，错落红尘中能够与你心灵相约是一种幸福。

24、爱上他，爱上与他一起听歌的日子，爱上那些至情演唱的悲情歌曲，每一首，每一句，每一字，都活生生地牵引着两颗遥不可及的心。爱在天边，爱人在天边，爱的故事在天边。如何，才能爱情重新来过，不要只留下一些伤痕。