CN105260986B - A kind of image magnification method of anti - Google Patents
A kind of image magnification method of anti Download PDFInfo
- Publication number
- CN105260986B CN105260986B CN201510669656.7A CN201510669656A CN105260986B CN 105260986 B CN105260986 B CN 105260986B CN 201510669656 A CN201510669656 A CN 201510669656A CN 105260986 B CN105260986 B CN 105260986B
- Authority
- CN
- China
- Prior art keywords
- image
- sharpening
- interpolation
- input picture
- magnification
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 39
- 230000003321 amplification Effects 0.000 claims abstract description 13
- 238000003199 nucleic acid amplification method Methods 0.000 claims abstract description 13
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000001914 filtration Methods 0.000 claims description 3
- 239000011159 matrix material Substances 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 15
- 238000003707 image sharpening Methods 0.000 abstract description 4
- 238000011156 evaluation Methods 0.000 abstract 1
- 238000010586 diagram Methods 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 4
- 230000000750 progressive effect Effects 0.000 description 2
- 238000012952 Resampling Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000006835 compression Effects 0.000 description 1
- 238000007906 compression Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- MJIHNNLFOKEZEW-UHFFFAOYSA-N lansoprazole Chemical compound CC1=C(OCC(F)(F)F)C=CN=C1CS(=O)C1=NC2=CC=CC=C2N1 MJIHNNLFOKEZEW-UHFFFAOYSA-N 0.000 description 1
- 230000014759 maintenance of location Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformations in the plane of the image
- G06T3/40—Scaling of whole images or parts thereof, e.g. expanding or contracting
- G06T3/4084—Scaling of whole images or parts thereof, e.g. expanding or contracting in the transform domain, e.g. fast Fourier transform [FFT] domain scaling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/73—Deblurring; Sharpening
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10016—Video; Image sequence
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20172—Image enhancement details
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Image Processing (AREA)
- Facsimile Image Signal Circuits (AREA)
Abstract
Description
技术领域technical field
本发明属于图像处理技术领域,涉及一种图像插值放大方法,具体涉及一种抗模糊的图像放大方法。The invention belongs to the technical field of image processing, and relates to an image interpolation enlarging method, in particular to an anti-blurring image enlarging method.
技术背景technical background
当前,数码相机或摄像头可以轻松地拍摄高分辨率的视频,但出于节省存储或传输成本的考虑,在视频监控、视频会议以及网络电视等应用场合依然存在大量的低分辨率视频内容。视频放大技术因而被广泛地用于提升图像的空间分辨率,以适应屏幕实际显示区域的要求。一些特殊的应用,例如视频监控,还涉及到对图像局部区域进行放大以便能辨识图像的细节内容。Currently, digital cameras or cameras can easily capture high-resolution videos, but for the sake of saving storage or transmission costs, there are still a large amount of low-resolution video content in applications such as video surveillance, video conferencing, and Internet TV. Video upscaling technology is therefore widely used to improve the spatial resolution of images to meet the requirements of the actual display area of the screen. Some special applications, such as video surveillance, also involve zooming in on a local area of the image so that details of the image can be identified.
大量的视频图像缩放技术被发展起来,从简单的插值到复杂的图像超分辨率。图像超分辨率技术尽管可以获得更清晰的放大效果,但运算复杂度居高不下;更不方便的是,图像超分辨率一般都要事先训练样本库,在实际应用中缺乏普适性和可操作性。因此,目前的视频应用软件或媒体播放器中采用的图像放大技术主要以简单、快速的插值方法为主,例如双线性插值(Bilinear)、双立方插值(Bicubic)、兰兹插值(Lanczos)等。然而,这些经典的插值放大方法同时也存在一个严重的缺陷,当原始图像清晰度不高(例如经过压缩的图像)或放大倍数过大时,插值放大后的图像存在明显的模糊效应,影响观看者的主观体验。A large number of video image scaling techniques have been developed, ranging from simple interpolation to complex image super-resolution. Although image super-resolution technology can obtain a clearer zoom-in effect, the computational complexity remains high; what is more inconvenient is that image super-resolution generally requires training sample libraries in advance, which lacks universality and scalability in practical applications. operability. Therefore, the current image enlargement technology used in video application software or media players is mainly based on simple and fast interpolation methods, such as bilinear interpolation (Bilinear), bicubic interpolation (Bicubic), Lanczos interpolation (Lanczos) Wait. However, these classic interpolation and amplification methods also have a serious defect. When the original image is not clear (such as a compressed image) or the magnification is too large, the image after interpolation and amplification has obvious blurring effect, which affects viewing. the subjective experience of the reader.
空间锐化滤波器是一种高通滤波器,可以强化数字图像的高频分量,恰好与插值滤波的低通作用相反。因此,如果在图像插值放大之前安插一个锐化操作,将有可能一定程度上抵消插值带来的模糊效应。此外,图像单次放大的倍数越高越易造成模糊,相反,放大倍数越低模糊越轻。因此,如果将高倍数放大过程转换为若干个低倍数的逐级放大,将有助于放大图像质量的提升。基于这两个角度,本发明提出一种抗模糊的图像放大方法。The spatial sharpening filter is a high-pass filter that can strengthen the high-frequency components of the digital image, just opposite to the low-pass effect of the interpolation filter. Therefore, if a sharpening operation is inserted before image interpolation and magnification, it will be possible to offset the blurring effect caused by interpolation to a certain extent. In addition, the higher the single magnification of the image, the easier it is to cause blur, on the contrary, the lower the magnification, the lighter the blur. Therefore, if the high magnification process is converted into several low magnification steps, it will help to improve the quality of the magnified image. Based on these two angles, the present invention proposes an anti-blurring image enlargement method.
发明内容Contents of the invention
为了解决上述技术问题,本发明将图像锐化运算和插值运算级联成一个整体,通过锐化预处理加强图像的轮廓细节,为插值运算贡献高频成分提升的信号源,从而消除插值过程的低通模糊效应影响。In order to solve the above technical problems, the present invention cascades the image sharpening operation and the interpolation operation into a whole, enhances the contour details of the image through sharpening preprocessing, and contributes the signal source of high-frequency component enhancement to the interpolation operation, thereby eliminating the interpolation process. Low pass blur effect effect.
本发明所采用的技术方案是:一种抗模糊的图像放大方法,其特征在于,包括以下步骤:The technical solution adopted in the present invention is: a kind of anti-blurring image enlargement method, it is characterized in that, comprises the following steps:
步骤1:采用图像的梯度特征衡量图像模糊程度,计算输入图像的模糊度指标;Step 1: Use the gradient feature of the image to measure the blur degree of the image, and calculate the blur index of the input image;
步骤2:综合输入图像的模糊度指标、总体放大倍数、图像分辨率三个要素,计算输入图像的锐化强度参数;Step 2: Calculate the sharpening intensity parameter of the input image by integrating the three elements of the blur index, the overall magnification, and the image resolution of the input image;
步骤3:根据计算得到的锐化强度常数,调用锐化算法对输入图像进行锐化;Step 3: Call the sharpening algorithm to sharpen the input image according to the calculated sharpening intensity constant;
步骤4:将总体放大倍数为R的放大操作分解为若干个小倍率的逐级放大,确定逐级放大的执行次数n;Step 4: Decompose the enlargement operation with the overall enlargement factor of R into a number of progressive enlargements with small magnifications, and determine the execution times n of successive enlargements;
步骤5:执行n次逐级插值放大运算。Step 5: Perform n times of step-by-step interpolation and amplification operations.
作为优选,步骤1中所述的模糊度指标的计算,采用如下方法:Preferably, the calculation of the ambiguity index described in step 1 adopts the following method:
将模糊度指标Fb定义为能量归一化的梯度:Define the ambiguity index Fb as the energy-normalized gradient:
其中,Gx、Gy分别表示水平和垂直梯度图,通过Sobel、Roberts或Prewitt梯度算子求取;E为输入图像的能量,用输入图像像素I计算“.*”表示矩阵点积运算。Among them, Gx and Gy respectively represent the horizontal and vertical gradient maps, obtained by Sobel, Roberts or Prewitt gradient operators; E is the energy of the input image, calculated with the input image pixel I ".*" means matrix dot product operation.
作为优选,步骤2中所述的锐化强度参数λ的计算,采用如下方法:As a preference, the calculation of the sharpening intensity parameter λ described in step 2 adopts the following method:
其中,Fb为模糊度指标,R是总体放大倍数,S为输入图像的面积,等于长乘高,Scif为一以CIF格式图像面积为基准的常数。Among them, Fb is the blur index, R is the overall magnification, S is the area of the input image, which is equal to the length multiplied by the height, and Scif is a constant based on the area of the CIF format image.
作为优选,步骤3中所述的锐化操作,是将输入图像的高通滤波版本经过调校后叠加到原始输入图像上,计算公式为:Preferably, the sharpening operation described in step 3 is to superimpose the high-pass filtered version of the input image on the original input image after adjustment, and the calculation formula is:
y(n,m)=x(n,m)+λz(n,m);y(n,m)=x(n,m)+λz(n,m);
这里,x,y分别表示原始输入图像和锐化后的输入图像,z表示高通滤波后的输入图像,用二维Laplacian微分算子获得,λ为锐化强度参数。Here, x and y represent the original input image and the sharpened input image respectively, z represents the input image after high-pass filtering, which is obtained by a two-dimensional Laplacian differential operator, and λ is the sharpening intensity parameter.
作为优选,步骤4中所述的确定逐级放大的执行次数n,采用如下规则:As a preference, the determination of the execution times n of step-by-step amplification described in step 4 adopts the following rules:
每次将图像的宽度和高度放大1.2倍,若总体放大倍数为R,则每次放大1.2倍、逐次放大到R倍的执行次数n计算为符号表示下取整。Each time the width and height of the image are enlarged by 1.2 times, if the overall magnification factor is R, then the execution times n of enlarging 1.2 times each time and enlarging to R times successively is calculated as symbol Indicates rounding down.
作为优选,步骤5中所述的执行n次逐级插值放大运算,其插值方法包括但不限于双线性插值法、双立方插值法、兰兹插值法。Preferably, in step 5, performing n times of step-by-step interpolation and amplification operations, the interpolation methods include but not limited to bilinear interpolation, bicubic interpolation, and Lanz interpolation.
与现有的图像放大方案相比,本发明具有以下优点和积极效果:Compared with the existing image enlargement scheme, the present invention has the following advantages and positive effects:
(1)本发明在简单的插值方法的前面施加一个锐化预处理,在不明显增加运算复杂度的前提下,有效消除了插值方法固有的模糊效应,显著改善了放大图像的清晰度;(1) The present invention applies a sharpening pretreatment in front of the simple interpolation method, effectively eliminates the inherent fuzzy effect of the interpolation method, and significantly improves the clarity of the enlarged image without significantly increasing the computational complexity;
(2)与复杂的超分辨率方案相比,本发明方法不牵涉样本图像训练,具有实现简单,处理效率高,普适性强的优点;(2) Compared with complex super-resolution schemes, the method of the present invention does not involve sample image training, and has the advantages of simple implementation, high processing efficiency, and strong universality;
(3)本发明方法基于的锐化算法和插值算法均来自成熟技术,便于组装现有的模块集成实现。(3) Both the sharpening algorithm and the interpolation algorithm based on the method of the present invention come from mature technologies, which are convenient for assembling existing modules for integration and realization.
附图说明Description of drawings
图1:本发明实施例的处理流程图;Fig. 1: processing flowchart of the embodiment of the present invention;
图2:本发明实施例的放大效果对比示例图,其中(a)为原始图像经普通双线性放大4倍效果图,(b)为原始图像经本发明的方法放大4倍效果图,(c)为解码图像经普通双线性放大4倍效果图,(d)为解码图像经本发明的方法放大4倍效果图。Fig. 2: the comparison example diagram of the magnification effect of the embodiment of the present invention, wherein (a) is the original image enlarged by 4 times through the ordinary bilinear effect diagram, (b) is the original image enlarged by the 4 times effect diagram by the method of the present invention, ( c) is the 4-fold effect diagram of the decoded image enlarged by ordinary bilinear, and (d) is the 4-fold effect diagram of the decoded image enlarged by the method of the present invention.
具体实施方式Detailed ways
为了便于本领域普通技术人员理解和实施本发明,下面结合附图及实施例对本发明作进一步的详细描述,应当理解,此处所描述的实施示例仅用于说明和解释本发明,并不用于限定本发明。In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the implementation examples described here are only used to illustrate and explain the present invention, and are not intended to limit this invention.
本发明提供的一种抗模糊的图像放大方法,将图像锐化运算和插值运算级联成一个整体。锐化操作的强度应与输入图像自身质量相适应,清晰的图像应弱锐化,模糊的图像应强锐化,否则,锐化不仅不能为后续的插值操作提供高频成分增强的预处理图像,反而会损伤图像、降低视觉保真度。导致图像模糊的因素来自多个方面,如目标和摄像机的相对运动产生的运动模糊,镜头散焦,大气散射,图像压缩、降噪和重采样等滤波过程,等等。这些过程无一例外地造成图像细节丢失及削弱目标边缘轮廓梯度,因此,采用图像的梯度特征刻画图像的模糊程度,清晰图像的梯度图锐利,模糊图像的梯度图平坦。The invention provides an anti-blurring image enlargement method, which cascades image sharpening operations and interpolation operations into a whole. The intensity of the sharpening operation should be adapted to the quality of the input image itself. Clear images should be weakly sharpened, and blurred images should be strongly sharpened. Otherwise, sharpening will not only fail to provide preprocessed images with enhanced high-frequency components for subsequent interpolation operations , which will damage the image and reduce visual fidelity. The factors that cause image blur come from many aspects, such as motion blur caused by the relative motion of the target and the camera, lens defocus, atmospheric scattering, image compression, noise reduction and resampling and other filtering processes, and so on. These processes without exception cause the loss of image details and weaken the gradient of the target edge contour. Therefore, the gradient feature of the image is used to describe the blur degree of the image. The gradient map of the clear image is sharp, and the gradient map of the blurred image is flat.
图像锐化算法通常用一个锐化强度参数控制锐化程度,对输入图像施加多大程度的锐化最少应考虑三个因素。首先,也是最重要的是图像的模糊程度,可用模糊度指标衡量;其次是图像的放大倍数,放大倍数越高越容易引起低通插值模糊效应,相应地应该执行更强的锐化来补偿放大的模糊效应;最后是输入图像自身的分辨率,分辨率越低的图像的细节保持度越差,越需要通过锐化来增强细节。Image sharpening algorithms usually use a sharpening strength parameter to control the degree of sharpening, and at least three factors should be considered for the degree of sharpening applied to the input image. The first and most important is the degree of blur of the image, which can be measured by the blur index; the second is the magnification of the image. The higher the magnification, the easier it is to cause the blur effect of low-pass interpolation. Accordingly, stronger sharpening should be performed to compensate for the magnification The blurring effect; the last is the resolution of the input image itself. The lower the resolution of the image, the worse the detail retention, and the more it needs to be sharpened to enhance the details.
基于以上考虑,本发明方法完整的处理流程如图1所示,包含以下步骤:Based on the above considerations, the complete processing flow of the method of the present invention is shown in Figure 1, comprising the following steps:
步骤1:采用图像的梯度特征衡量图像模糊程度,计算输入图像的模糊度指标;Step 1: Use the gradient feature of the image to measure the blur degree of the image, and calculate the blur index of the input image;
综合图像强度的影响,将模糊度指标Fb定义为能量归一化的梯度Integrating the influence of image intensity, the blur index Fb is defined as the energy normalized gradient
这里,Gx、Gy分别表示水平和垂直梯度图,可通过Sobel,Roberts,Prewitt等经典的梯度算子求取,E为输入图像的能量,用输入图像像素I计算“.*”表示矩阵点积运算。Here, Gx and Gy respectively represent the horizontal and vertical gradient maps, which can be obtained by classic gradient operators such as Sobel, Roberts, Prewitt, etc., E is the energy of the input image, and is calculated using the input image pixel I ".*" means matrix dot product operation.
步骤2:综合输入图像的模糊度指标、总体放大倍数、图像分辨率三个要素,计算输入图像的锐化强度参数;Step 2: Calculate the sharpening intensity parameter of the input image by integrating the three elements of the blur index, the overall magnification, and the image resolution of the input image;
结合经验数据,锐化强度参数λ的计算,采用如下方法:Combined with empirical data, the calculation of the sharpening intensity parameter λ adopts the following method:
其中,Fb为模糊度指标,R是总体放大倍数,S为输入图像的面积,等于长乘高,Scif为一以CIF格式图像面积(352x288)为基准的常数。Among them, Fb is the blur index, R is the overall magnification, S is the area of the input image, which is equal to the length multiplied by the height, and S cif is a constant based on the area of the CIF format image (352x288).
步骤3:根据计算得到的锐化强度常数,调用锐化算法对输入图像进行锐化;Step 3: Call the sharpening algorithm to sharpen the input image according to the calculated sharpening intensity constant;
本发明采用的锐化算法是,将输入图像图像的高通滤波版本经过调校后叠加到原始输入图像图像上,计算公式为The sharpening algorithm used in the present invention is to superimpose the high-pass filter version of the input image on the original input image after adjustment, and the calculation formula is
y(n,m)=x(n,m)+λz(n,m);y(n,m)=x(n,m)+λz(n,m);
这里,x,y分别表示原始输入图像图像和锐化后的输入图像图像,z表示高通滤波后的输入图像图像,用二维Laplacian微分算子获得,λ为锐化强度参数。Here, x and y represent the original input image and the sharpened input image respectively, z represents the high-pass filtered input image, which is obtained by a two-dimensional Laplacian differential operator, and λ is the sharpening intensity parameter.
步骤4:将总体放大倍数为R的放大操作分解为若干个小倍率的逐级放大,确定逐级放大的执行次数n;Step 4: Decompose the enlargement operation with the overall enlargement factor of R into a number of progressive enlargements with small magnifications, and determine the execution times n of successive enlargements;
每次将图像的宽度和高度放大1.2倍,若总体放大倍数为R,则每次放大1.2倍、逐次放大到R倍的执行次数n计算为符号表示下取整。Each time the width and height of the image are enlarged by 1.2 times, if the overall magnification factor is R, then the execution times n of enlarging 1.2 times each time and enlarging to R times successively is calculated as symbol Indicates rounding down.
步骤5:执行n次逐级插值运算,可供选择的插值方法包括但不限于双线性插值、双立方插值、兰兹插值。Step 5: Perform n times of step-by-step interpolation operations, and the interpolation methods available for selection include but are not limited to bilinear interpolation, bicubic interpolation, and Ranz interpolation.
应当理解的是,本说明书未详细阐述的部分均属于现有技术。It should be understood that the parts not described in detail in this specification belong to the prior art.
应当理解的是,上述针对较佳实施例的描述较为详细,并不能因此而认为是对本发明专利保护范围的限制,本领域的普通技术人员在本发明的启示下,在不脱离本发明权利要求所保护的范围情况下,还可以做出替换或变形,均落入本发明的保护范围之内,本发明的请求保护范围应以所附权利要求为准。It should be understood that the above-mentioned descriptions for the preferred embodiments are relatively detailed, and should not therefore be considered as limiting the scope of the patent protection of the present invention. Within the scope of protection, replacements or modifications can also be made, all of which fall within the protection scope of the present invention, and the scope of protection of the present invention should be based on the appended claims.
Claims (5)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201510669656.7A CN105260986B (en) | 2015-10-13 | 2015-10-13 | A kind of image magnification method of anti |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201510669656.7A CN105260986B (en) | 2015-10-13 | 2015-10-13 | A kind of image magnification method of anti |
Publications (2)
Publication Number | Publication Date |
---|---|
CN105260986A CN105260986A (en) | 2016-01-20 |
CN105260986B true CN105260986B (en) | 2018-06-29 |
Family
ID=55100661
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201510669656.7A Active CN105260986B (en) | 2015-10-13 | 2015-10-13 | A kind of image magnification method of anti |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN105260986B (en) |
Families Citing this family (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN106709873B (en) * | 2016-11-11 | 2020-12-18 | 浙江师范大学 | A Super-Resolution Method Based on Cubic Spline Interpolation and Iterative Update |
CN106875350A (en) * | 2017-01-05 | 2017-06-20 | 宇龙计算机通信科技(深圳)有限公司 | Method, device and the terminal of sharpening treatment are carried out to blurred picture |
CN111737665A (en) * | 2019-04-27 | 2020-10-02 | 常英梅 | Grading display method for handheld mobile terminal |
CN110446071A (en) * | 2019-08-13 | 2019-11-12 | 腾讯科技(深圳)有限公司 | Multi-media processing method, device, equipment and medium neural network based |
CN111028182B (en) * | 2019-12-24 | 2024-04-26 | 北京金山云网络技术有限公司 | Image sharpening method, device, electronic equipment and computer readable storage medium |
CN111245816B (en) * | 2020-01-08 | 2021-01-26 | 山东挚友企服信息科技有限公司 | Data uploading system and method based on content detection |
CN111654627B (en) * | 2020-06-09 | 2021-11-26 | 展讯通信(上海)有限公司 | Digital zooming method, device, equipment and storage medium |
CN112037135B (en) * | 2020-09-11 | 2023-06-09 | 上海瞳观智能科技有限公司 | Method for magnifying and displaying selected image key main body |
Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6421468B1 (en) * | 1999-01-06 | 2002-07-16 | Seiko Epson Corporation | Method and apparatus for sharpening an image by scaling elements of a frequency-domain representation |
WO2007027893A2 (en) * | 2005-08-30 | 2007-03-08 | The Regents Of The University Of California, Santa Cruz | Kernel regression for image processing and reconstruction |
CN102547067A (en) * | 2011-12-31 | 2012-07-04 | 中山大学 | Improved bicubic interpolation video scaling method |
CN102844786A (en) * | 2010-03-01 | 2012-12-26 | 夏普株式会社 | Image enlargement device, image enlargement program, memory medium on which image enlargement program is stored, and display device |
CN103299609A (en) * | 2011-01-07 | 2013-09-11 | Tp视觉控股有限公司 | Method for converting input image data into output image data, image conversion unit for converting input image data into output image data, image processing apparatus, display device |
CN104933679A (en) * | 2015-07-06 | 2015-09-23 | 福州瑞芯微电子有限公司 | A method for enlarging an image and a system corresponding to the method |
-
2015
- 2015-10-13 CN CN201510669656.7A patent/CN105260986B/en active Active
Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6421468B1 (en) * | 1999-01-06 | 2002-07-16 | Seiko Epson Corporation | Method and apparatus for sharpening an image by scaling elements of a frequency-domain representation |
WO2007027893A2 (en) * | 2005-08-30 | 2007-03-08 | The Regents Of The University Of California, Santa Cruz | Kernel regression for image processing and reconstruction |
CN102844786A (en) * | 2010-03-01 | 2012-12-26 | 夏普株式会社 | Image enlargement device, image enlargement program, memory medium on which image enlargement program is stored, and display device |
CN103299609A (en) * | 2011-01-07 | 2013-09-11 | Tp视觉控股有限公司 | Method for converting input image data into output image data, image conversion unit for converting input image data into output image data, image processing apparatus, display device |
CN102547067A (en) * | 2011-12-31 | 2012-07-04 | 中山大学 | Improved bicubic interpolation video scaling method |
CN104933679A (en) * | 2015-07-06 | 2015-09-23 | 福州瑞芯微电子有限公司 | A method for enlarging an image and a system corresponding to the method |
Also Published As
Publication number | Publication date |
---|---|
CN105260986A (en) | 2016-01-20 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN105260986B (en) | A kind of image magnification method of anti | |
CN110163237B (en) | Model training and image processing method, device, medium and electronic equipment | |
US8275218B2 (en) | Forward and backward image resizing method | |
US8131117B2 (en) | Method for magnifying images and videos and associated image processing device | |
US8144253B2 (en) | Multi-frame approach for image upscaling | |
TWI543586B (en) | Method for image enhancement, and image processing apparatus and computer program product using the same | |
TWI406571B (en) | Image Processing Method for Enhancing Image Edge Resolution | |
Fang et al. | Antialiasing filter design for subpixel downsampling via frequency-domain analysis | |
US20100260433A1 (en) | System and method for scaling images | |
CN111353955A (en) | Image processing method, device, equipment and storage medium | |
US10868969B1 (en) | Method and apparatus for accelerated tonemapping and display | |
WO2011108144A1 (en) | Image enlargement device, image enlargement program, memory medium on which an image enlargement program is stored, and display device | |
CN102547067B (en) | Improved bicubic interpolation video scaling method | |
Dai et al. | Wavelet-based network for high dynamic range imaging | |
CN106558021B (en) | Video enhancement method based on super-resolution technology | |
Sakurai et al. | Super-resolution through non-linear enhancement filters | |
WO2014102876A1 (en) | Image processing device and image processing method | |
Kraus et al. | GPU-based edge-directed image interpolation | |
CN102547068B (en) | Improved Bilinear Interpolation Video Scaling Method | |
CN111091506A (en) | Image processing method and device, storage medium and electronic equipment | |
TW201437966A (en) | Adaptive image edge amending apparatus and method thereof | |
TW201032592A (en) | Method for upscaling images and videos and associated image processing device | |
KR101332030B1 (en) | Method of magnifying image and computer readable medium storing the program thereof | |
JP2004282593A (en) | Contour correction device | |
Hu et al. | Non-stationary content-adaptive projector resolution enhancement |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
C06 | Publication | ||
PB01 | Publication | ||
C10 | Entry into substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant | ||
TR01 | Transfer of patent right |
Effective date of registration: 20200930 Address after: 519000 unit 1, No. 33, Haihe street, Hengqin New District, Zhuhai City, Guangdong Province Patentee after: ZHUHAI DAHENGQIN TECHNOLOGY DEVELOPMENT Co.,Ltd. Address before: 430072 Hubei Province, Wuhan city Wuchang District of Wuhan University Luojiashan Patentee before: WUHAN University |
|
TR01 | Transfer of patent right |