CN105139366A - Image enhancement method based on space domain - Google Patents
Image enhancement method based on space domain Download PDFInfo
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- CN105139366A CN105139366A CN201510609792.7A CN201510609792A CN105139366A CN 105139366 A CN105139366 A CN 105139366A CN 201510609792 A CN201510609792 A CN 201510609792A CN 105139366 A CN105139366 A CN 105139366A
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Abstract
The invention discloses an image enhancement method based on space domain and belongs to the technical field of image enhancement methods. Based on index for measuring image quality, advantages and disadvantages of the image enhancement method are evaluated. The method comprises steps of 1) obtaining an original image; 2) performing grey level transformation on the original image so as to obtain a grey level image; 3) performing histogram equalization on the grey level image; and 4) performing histogram specification enhancement processing on the grey level image processed via the histogram equalization. The method is used for image processing.
Description
Technical field
Based on an image enchancing method for spatial domain, for image procossing, belonging to image increases method and technology field.
Background technology
Image enhaucament refers to the important information according to specifically needing in outstanding image, weakens simultaneously or removes unwanted information.From the image that different approaches obtains, by carrying out suitable enhancing process, the smudgy original image even at all cannot differentiated of script can be processed into the used image being rich in a large amount of useful information clearly, effectively remove the edge in the noise in image, enhancing image or other interested regions, thus be more prone to detect target interested in image and measure.
The object of image enhaucament is the visual effect strengthening image, original image is converted to a kind of form being more suitable for eye-observation and Computer Analysis process.It generally by the visual characteristic of human eye, will seem good visual effect to obtain, seldom relates to objective and unified evaluation criterion.The effect strengthened usually all with concrete image-related system, the subjective sensation by people is evaluated.
Computer Image Processing developing history is not long, starts from the sixties in 20th century, is developed so far.The image processing techniques of 21 century is about to, towards the development of high quality aspect, realizing the real-time process of image now, and adopt Digital Holography to make image comprise information the most complete and abundant, the intelligence realizing image generates, processes, understands and identifies.
Image enhaucament is as the important component part of image procossing, and traditional image enchancing method has played vital role for improving picture quality.Along with deepening continuously to image enhancement technique research, new image enchancing method also constantly emerges in large numbers.Mainly be divided at present traditional image enchancing method, based on the image enchancing method of multiscale analysis and mathematical morphology Enhancement Method etc.
Due to the current quality also not having a kind of index of general measurement picture quality can be used for evaluating image enchancing method, image enhaucament theory needs to be further improved.Also just because of this, the exploration of image enhancement technique has experimental and diversity.The method strengthened is often also pointed, to such an extent as to may not be applicable to another kind of image to the good Enhancement Method of certain class image effect.In a practical situation, a kind of effective method being found to test widely, when not having Given Graph picture how to be lowered the priori of quality, predict that the effectiveness of certain concrete grammar is very difficult.So the method often adopted uses the combination of several groups of enhancing technology or the method for adjustment in use parameter.And want the enhancing effect of an acquisition satisfaction, often need man-machine reciprocation.
Summary of the invention
The present invention is directed to the deficiencies in the prior art part and provide a kind of image enchancing method based on spatial domain, evaluate the quality of image enchancing method by the index weighing picture quality.
To achieve these goals, the technical solution used in the present invention is:
Based on an image enchancing method for spatial domain, it is characterized in that:
(1) original image is obtained;
(2) original image is carried out greyscale transformation, obtain gray level image;
(3) gray level image is carried out histogram equalization;
(4) histogram specification is carried out to the gray level image carrying out histogram equalization process and increase process.
Further, in described step (2), original image is carried out greyscale transformation, obtains gray level image as follows:
If the gray-scale value D=f (x of original image pixel, y), the gray-scale value D '=g (x of image pixel after process, y), then grey level enhancement can be expressed as: g (x, y)=T [f (x, ] or D '=T (D) y), require that D and D ' is within the tonal range of image, function T is called greyscale transformation function, it is described that the transformational relation between input gray level value and output gray level value.
Further, in described step (3), by as follows for the concrete steps that gray level image carries out histogram equalization:
(31) input gray level image;
(32) according to formula P
r(r
k)=n
k/ m*n (k=0,1,2 ..., L-1) and calculate the probability that corresponding grey scale level occurs, draw the histogram of gray scale picture;
(33) the gray level cumulative distribution function of gray scale image is calculated: sk=Σ p
r(r
k);
(34) Sk=round ((S1*256)+0.5) is rounded; Sk is normalized to close gray level, draws the histogram after equalization;
(35) gray-scale value after each pixel normalization is assigned to this pixel, exports the image after equalization.
Further, in described step (4), the concrete steps of the gray level image carrying out histogram equalization process being carried out to histogram specification increase process are as follows:
(41) obtain the grey level probability density function of the gray level image after to histogram equalization, try to achieve transforming function transformation function s=T (r)=∫
0 rp
r(r) dr;
(42) the gray level s obtained does inverse transformation z=G-1 (s).
Compared with prior art, the invention has the advantages that:
One, the present invention can by the quality weighing the index of picture quality and evaluate image enchancing method, widely applicable, without the need to man-machine mutual, and measurable increase effect;
Two, carry out image gray-scale transformation, can image quality be improved, make the display effect of image more clear;
Three, histogram equalization process, make gray scale present two ends distribution, have the image of more pixel to process in the low gray areas of image, that can improve image retracts effect simultaneously;
Four, image is after histogram specification process, increases effect and is conducive to the vision interpretation of people or is convenient to machine recognition.
Accompanying drawing explanation
Fig. 1 is schematic flow sheet of the present invention.
Embodiment
Below in conjunction with drawings and Examples, the invention will be further described.
In image procossing, spatial domain refers to the space be made up of pixel.Space domain is then directly act on pixel in image or its certain field.Thus, spatial domain image enchancing method is divided into pixel process and field process.
Pixel process comprises image gray-scale transformation and histogram equalization.Wherein histogram equalization can be subdivided into traditional histogram equalization and local histogram equalization again.Field process then comprises image smoothing filtering technique and image sharpening filtering.Wherein will relate to the contents such as mean filter, median filter, gradient algorithm, Laplace operator.
Based on an image enchancing method for spatial domain, comprise the steps:
(1) original image is obtained;
(2) original image is carried out greyscale transformation, obtain gray level image; Original image is carried out greyscale transformation, obtains gray level image as follows:
If the gray-scale value D=f (x of original image pixel, y), the gray-scale value D '=g (x of image pixel after process, y), then grey level enhancement can be expressed as: g (x, y)=T [f (x, ] or D '=T (D) y), require that D and D ' is within the tonal range of image, function T is called greyscale transformation function, it is described that the transformational relation between input gray level value and output gray level value.Greyscale transformation is divided into linear transformation and nonlinear transformation.Binaryzation and thresholding step are:
Set a certain threshold value T, with T, the data of image are divided into two large divisions: be greater than the pixel group of T and be less than the pixel group of T.This studies the most special method of greyscale transformation, is called the binaryzation of image.The operating process of threshold process is first specified by user or generates a threshold value by algorithm, if the gray-scale value of certain pixel is less than this threshold value in image, then the gray-scale value of this pixel is set to 0 or 255, otherwise gray-scale value is set to 255 or 0.The transforming function transformation function expression formula of thresholding is as follows:
f(x)＝0x<T
f(x)＝255x>T
Wherein T is the threshold value of specifying.Threshold value T is just as individual threshold, and larger than it is exactly white, and less than it is exactly black.This transforming function transformation function is step function, and only need provide threshold point T, the image after threshold process becomes a width black and white binary map, and threshold process is a kind of common method that gray-scale map turns binary map.
(3) gray level image is carried out histogram equalization; By as follows for the concrete steps that gray level image carries out histogram equalization:
(31) input gray level image;
(32) according to formula P
r(r
k)=n
k/ m*n (k=0,1,2 ..., L-1) and calculate the probability that corresponding grey scale level occurs, draw the histogram of gray scale picture;
(33) the gray level cumulative distribution function of gray scale image is calculated: sk=Σ p
r(r
k);
(34) Sk=round ((S1*256)+0.5) is rounded; Sk is normalized to close gray level, draws the histogram after equalization;
(35) gray-scale value after each pixel normalization is assigned to this pixel, exports the image after equalization.
(4) histogram specification is carried out to the gray level image carrying out histogram equalization process and increase process.The concrete steps of the gray level image carrying out histogram equalization process being carried out to histogram specification increase process are as follows:
(41) obtain the grey level probability density function of the gray level image after to histogram equalization, try to achieve transforming function transformation function s=T (r)=∫
0 rp
r(r) dr;
(42) the gray level s obtained does inverse transformation z=G-1 (s).
Gray level through processing the image obtained above will have the probability density function of regulation.Adopt the raw image data identical with histogram equalization (64 × 64 pixels and have 8 grades of gray scales), the main difficulty utilizing histogram specification method to carry out image enhaucament is to form significant histogram.Image is through histogram specification, and it strengthens effect and will be conducive to the vision interpretation of people or be convenient to machine recognition.
Claims (4)
1., based on an image enchancing method for spatial domain, it is characterized in that:
(1) original image is obtained;
(2) original image is carried out greyscale transformation, obtain gray level image;
(3) gray level image is carried out histogram equalization;
(4) histogram specification is carried out to the gray level image carrying out histogram equalization process and increase process.
2. a kind of image enchancing method based on spatial domain according to claim 1, is characterized in that: in described step (2), original image is carried out greyscale transformation, obtains gray level image as follows:
If the gray-scale value D=f (x of original image pixel, y), the gray-scale value D '=g (x of image pixel after process, y), then grey level enhancement can be expressed as: g (x, y)=T [f (x, ] or D '=T (D) y), require that D and D ' is within the tonal range of image, function T is called greyscale transformation function, it is described that the transformational relation between input gray level value and output gray level value.
3. a kind of image enchancing method based on spatial domain according to claim 1, is characterized in that: in described step (3), by as follows for the concrete steps that gray level image carries out histogram equalization:
(31) input gray level image;
(32) according to formula P
r(r
k)=n
k/ m*n (k=0,1,2 ..., L-1) and calculate the probability that corresponding grey scale level occurs, draw the histogram of gray scale picture;
(33) the gray level cumulative distribution function of gray scale image is calculated: sk=Σ p
r(r
k);
(34) Sk=round ((S1*256)+0.5) is rounded; Sk is normalized to close gray level, draws the histogram after equalization;
(35) gray-scale value after each pixel normalization is assigned to this pixel, exports the image after equalization.
4. a kind of image enchancing method based on spatial domain according to claim 1, is characterized in that: in described step (4), and the concrete steps of the gray level image carrying out histogram equalization process being carried out to histogram specification increase process are as follows:
(41) obtain the grey level probability density function of the gray level image after to histogram equalization, try to achieve transforming function transformation function s=T (r)=∫
0 rp
r(r) dr;
(42) the gray level s obtained does inverse transformation z=G-1 (s).
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| CN106169181A (en) * | 2016-06-30 | 2016-11-30 | 北京奇艺世纪科技有限公司 | A kind of image processing method and system |
| CN106251360A (en) * | 2016-08-23 | 2016-12-21 | 湖南文理学院 | Thresholding Method for Grey Image Segmentation based on arithmetic geometry divergence |
| CN106295664A (en) * | 2016-08-02 | 2017-01-04 | 沈阳地泰检测设备有限公司 | A kind of processing method of postal sorting system acquired image |
| CN106303252A (en) * | 2016-08-26 | 2017-01-04 | 合肥若涵信智能工程有限公司 | Compensation wireless monitoring video camera |
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