Image histogram is a graphical representation of one form of the Spectral Characteristics of the respective citrayang. With the histogram, image analysis, image understanding that can be learned, for example aspects of brightness and sharp. With the histogram, image analysis, image understanding That Can be learned, for example aspects of brightness and sharp. Dari histogram juga kadang – kadang dapat diduga jenis saluran spectral citra yang digunakan. From the histogram is also sometimes - sometimes it can be predicted spectral channel image types used. Perubaha atas distribusi nilai pada citra secara langsung berakibat pada perubahan tampilan histogram. Perubaha for the distribution of values in the images directly resulted in changes of the histogram display. From the histogram is also sometimes - sometimes it can be predicted spectral channel image types used. Also from the histogram is Sometimes - Sometimes it Can Be Predicted spectral channel image types used. Perubaha for the distribution of values in the images directly resulted in changes of the histogram display. Perubaha for the distribution of values in the images directly resulted in changes of the histogram display. Sebaliknya, dengan memainkan bentuk histogramnya,banyak program pengolahan citra secar interaktif mampu mengubah tampilan citranya.Dengan kata lain, perangkat lunak pengolah citra kadang – kadang menggunakan histogram sebagai jembatan komunikasi antara pengguna dengan citra. Instead, by playing shape histogramnya, many secar interactive image processing program capable of changing the look citranya.Dengan other words, the image processing software sometimes - sometimes using the histogram as a communication bridge between users with the image. Instead, by playing shape histogramnya, many image processing programs can change the look of interactive secar citranya.Dengan other words, the image processing software sometimes - sometimes using the histogram as a communication bridge between users with the image. Instead, by playing shape histogramnya, many image processing programs, Can change the look of interactive secar citranya.Dengan other words, the image processing software Sometimes - Sometimes using the histogram as a communication bridge Between users with the image.
Histogram adalah suatu gambaran distribusi nilai piksel pada suatu potongan citra, yang disertai dengan frekuensi kemunculan setiap nilai. Histogram is a picture of the distribution of pixel values on a piece of imagery, coupled with the frequency of occurrence of each value. Histogram is a picture of the distribution of pixel values on a piece of imagery, coupled with the frequency of occurrence of each value. Histogram is a picture of the distribution of pixel values on a piece of imagery, coupled with the frequency of occurrence of Each value. Histogram citra dipresentasikan dengan dua bentuk : Image histogram is presented in two forms: Image histogram is presented in two forms: Image histogram is Presented in two forms:
1. 1. 1. 1. Table yang memuat piksel, presentase absolute setiap nilai, dengan presentase kumulatifnya. Table that includes pixel, an absolute percentage of each value, with the cumulative percentage. Table that includes pixel, an absolute percentage of each value, with the cumulative percentage. That table includes pixels, the absolute percentage of Each value, with the actual cumulative percentage.
2. 2. 2. 2. Gambar garfis yang menunjukan nilai piksel pada sumbu x dan frekuensi kemunculan pada sumbu y. Garfis images that show the value of pixels on the x axis and the frequency of occurrence on the y axis Garfis images that show the value of pixels on the x axis and the frequency of occurrence on the y axis That Garfis images show the value of pixels on the x axis and the frequency of occurrence on the y-axis
Melalui gambaran grafis histogram ini, secara umum dapat diketahui sifat – sifat citra yang diwakilinya. Misalnya, cita yang direkam dengan spectrum gelombang relative pendek akan menghasilkan 'bukit tunggal' histogram yang sempit (unimodal).Wilayah yang memuat tubuh air agak luas akan menghasilkan kenampakan histogram dengan dua puncak, apabila direkam pada spectrum inframerah dekat (bi-modal).Histogram unimodal yang sempit biasanya kurang mampu menyajikan kenampakn obyek secara tajam,sedangkan histogram yang gemuk (lebar) relative lebih tajam dibandingkan dengan yang sempit. Through a graphical representation of this histogram, can generally be known to nature - the nature of the image represents. For example, the ideals that were recorded with a relatively short-wave spectrum will produce a 'single hill' narrow histogram (unimodal). Areas that contain rather extensive body of water will result in the appearance histogram with two peaks, when recorded in the near infrared spectrum (bi-modal). Histogram unimodal narrow kenampakn usually less able to present a sharp object, while the fat histogram (width) relatively more sharply than narrow. Through a graphical representation of this histogram, can generally be known to nature - the nature of the image represents. Through a graphical representation of this histogram, can be Generally Known to nature - the nature of the image represents. For example, the ideals that were recorded with a relatively short-wave spectrum will produce a 'single hill' narrow histogram (unimodal). For example, the ideals That Were Recorded with a relatively short-wave spectrum will from Produce a 'single hill' narrow histogram (unimodal). Areas that contain rather extensive body of water will result in the appearance histogram with two peaks, when recorded in the near infrared spectrum (bi-modal). Areas That contain rather extensive body of water will of result in the appearance histogram with two peaks, Pls Recorded in the near infrared spectrum (bi-modal). Histogram unimodal narrow kenampakn usually less able to present a sharp object, while the fat histogram (width) relatively more sharply than narrow. Narrow unimodal histogram kenampakn usually less Able to present a sharp object, while the fat histogram (width) relatively more sharply than narrow.
Penajaman kontras citra melalui histogram dapat dlakukan dengan dua macam cara, yaitu perentangan kontras (Contrust stretching)dan ekualisasi histogram (histogram equalization). Perentangan kontras merupakan upaya mempertajam kenampakan citra dengan merentang nilai maksimum dan nilai minimum citra.kompresi citra, justru sebaliknya, dilakukan dengan memampatkan histogram, yaitu menggeser nilai minimumke nilai minimum baru yang lebih tinggi dan menggeser nilai maksimum ke nilai maksimum baru yang lebih rendah, sehingga histogramnya menjadi lebih 'langsing'.Berbeda halnya dengan perentangan kontras yang lebih linier, equalisasi histogram merupakan upaya penajaman ecara non-linier,yang menata kembali distribusi nilai piksel citra dalam bentuk histogram ke bentuk histogram yang baru, dimana dapat terjadi penggabungan beberapa nilai menjadi nilai baru dengan frekuensi kemunculan yang baru pula. Image contrast enhancement by histogram can dlakukan with two different ways, namely perentangan contrast (Contrust stretching) and histogram equalization (the histogram equalization). Perentangan an effort to sharpen the appearance contrasts with the image stretching maximum value and minimum value citra.kompresi image, quite the contrary, done to compress the histogram, which shifts the value minimumke new minimum value is higher and the maximum value shifts to a new maximum value is lower, so histogramnya become more 'slender'. perentangan Unlike the case with a more linear contrast, histogram equalization is an effort to sharpen non ecara , linear, which rearranges the distribution of the image pixel value in the form of a histogram to form a new histogram, which can occur merging multiple values into new values with the emergence of new frequencies as well. Image contrast enhancement by histogram can dlakukan with two different ways, namely perentangan contrast (Contrust stretching) and histogram equalization (histogram equalization). Image contrast enhancement by histogram with two Different cans dlakukan Airways, namely perentangan contrast (Contrust stretching) and histogram equalization (histogram equalization). Perentangan an effort to sharpen the appearance contrasts with the image stretching maximum value and minimum value citra.kompresi image, quite the contrary, done to compress the histogram, which shifts the value minimumke new minimum value is higher and the maximum value shifts to a new maximum value is lower, so histogramnya become more 'slender'. Perentangan an effort to Sharpen the appearance contrasts with the image stretching maximum value and minimum value citra.kompresi image, quite the contrary, done to compress the histogram, Which shifts the value minimumke new minimum value is higher and the maximum value shifts to a new maximum value is lower, so more histogramnya changed from 'Slender'. perentangan Unlike the case with a more linear contrast, histogram equalization is an effort to sharpen non ecara -linear, which rearranges the distribution of the image pixel value in the form of a histogram to form a new histogram, which can occur merging multiple values into new values with the emergence of new frequencies as well. perentangan Unlike the case with a more linear contrast, histogram equalization is an effort to Sharpen ecara non-linear, rearranges Which the distribution of the image pixel value in the form of a histogram to form a new histogram, Which Can occur merging multiple values into new values with the Emergence of new frequencies as well.
Penajaman citra bertujuan untuk meningkatkan mutu citra, baik untuk memperoleh keindahan gambar maupun untuk kepentingan analisis citra. Image enhancement aims to improve the quality of the image, either to obtain a picture of beauty as well as for image analysis. Image enhancement aims to improve the quality of the image, either to obtain a picture of beauty as well as for image analysis. AIMS image enhancement to improve the quality of the image, either to obtain a picture of beauty as well as for image analysis.
Lillisand and kiefer (1994, h.542) membagi teknik penajaman citra menjadi tiga macam, yaitu : Lillisand and Kiefer (1994, h.542) sharpening technique divides the image into three kinds, namely: Lillisand and Kiefer (1994, h.542) sharpening technique divides the image into three kinds, namely: Lillisand and Kiefer (1994, h.542) sharpening technique divides the image into three kinds, namely:
1. 1. 1. 1. Manipulasi Kontras Contrast manipulation Contrast Contrast manipulation manipulation
Pada teknik ini dilakukan proses pengubah tingkat kecerahan antar obyek pada citra, sehingga obyek akan lebih mudah dibedakan. In this technique made the process of altering the level of brightness between object on the image, so the object will be more easily distinguished. In this technique made the process of altering the level of brightness between object on the image, so the object will be more easily distinguished. In this technique made the process of altering the level of brightness Between object on the image, so the object will from some more Easily distinguished. Teknik penajaman yang termasuk dalam kelompok ini adalah penentuan ambang kecerahan (grey-level thresholding). Sharpening techniques included in this group is to determine the threshold of brightness (gray-level thresholding). Sharpening techniques included in this group is to determine the threshold of brightness (gray-level thresholding). Sharpening techniques Included in this group is to determine the threshold of brightness (gray-level thresholding).
2. 2. 2. 2. Manipulasi kenampakan spasial Manipulation of spatial appearance Manipulation of spatial manipulation of spatial appearance appearance
Teknik ini diterapkan untuk memperjelas atau mengurangi ketajaman niali kecerahan, sehingga akan mengubah kenampakan tekstural pada citra. This technique applied to clarify or reduce the sharpness of the brightness of the top-up terminal, so that will change the textural appearance of an image. This technique applied to clarify or reduce the sharpness of the brightness of the top-up terminal, so that will change the textural appearance of an image. This technique applied to Clarify or reduce the sharpness of the brightness of the top-up terminals, so That change will of the textural appearance of an image. Pengubahan nilai tiap piksel pada citra dilakukan dengan mempertimbangkan nilai piksel disekitarnya. Changing the value of each pixel in the image is done by considering the value of surrounding pixels. Teknik penajaman yang termasuk kelompok ini adalah pemfilteran spasial (spasial filtering), penajaman tepi (edge enhancement) dan analisis fourier (Fourier analisis). Sharpening techniques that include this group is the spatial filtering (spatial filtering), sharpening the edge (edge enhancement) and Fourier analysis (Fourier analysis). Changing the value of each pixel in the image is done by considering the value of surrounding pixels. Sharpening techniques that include this group is the spatial filtering (spatial filtering), sharpening the edge (edge enhancement) and Fourier analysis (Fourier analysis). Changing the value of Each pixel in the image is done by considering the value of surrounding pixels. Sharpening techniques That includes in this group is the spatial filtering (spatial filtering), sharpening the edge (edge enhancement) and Fourier analysis (Fourier analysis).
3. 3. 3. 3. Manipulasi citra jamak Plural image manipulation Plural plural image manipulation image manipulation
Teknik ini untuk mengatasi adanya bising (noise) pada citra saluran tunggal akibat pengaruh sumber - sumber spectral yang bervariasi dari liputan obyek yang terekam pada citra serta mengungkapkan tema - tema tertentu sesuai dengan hasil citra yang termanipulasikan. This technique to overcome the noise (noise) on a single channel image due to the influence of the source - the source spectral coverage, which varies from the recorded object on the image, and to express the theme - a particular theme in accordance with the results termanipulasikan image. This technique to overcome the noise (noise) on a single channel image due to the influence of the source - the source spectral coverage, which varies from the recorded object on the image, and to express the theme - a particular theme in accordance with the results termanipulasikan image. This technique to overcome the noise (noise) on a single channel image due to the influence of the source - the source spectral coverage, Which Varies Recorded from the object on the image, and to express the theme - a particular theme in accordance with the termanipulasikan image results. Teknik penajaman yang termasuk dalam kelompok ini antara lain adalah color composite, vegetation component, dan transfornasi HIS (Intensity-Hue-Saturation). Sharpening techniques included in this group include color composite, vegetation component, and transfornasi HIS (Intensity-Hue-Saturation). Sharpening techniques included in this group include color composite, vegetation component, and transfornasi HIS (Intensity-Hue-Saturation). Sharpening Included in this group techniques include color composite, vegetation component, and transfornasi HIS (Intensity-Hue-Saturation).
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Citra digital merupakan konfigurasi piksel yang bervariasi nilai spektralnya, dan membentuk suatu kenampakan kuasi-kontinu.Tiap kenampakn obyek berbeda satu sma lain karena adanya perbedaan interval nilai piksel yang mempresentasikannya, dan juga karena berbeda kesan pola spasial yang dihasilkannya. Digital image is a pixel configuration varying spectral values, and form a quasi-kontinu.Tiap kenampakn appearance of an object differ from one another because of differences sma interval of pixel values are present, and also because of different spatial patterns of the resulting impression. Digital image is a pixel configuration varying spectral values, and form a quasi-kontinu.Tiap kenampakn appearance of an object differ from one another because of differences sma interval of pixel values are present, and also because of different spatial patterns of the resulting impression. Digital image is a pixel configuration varying behavior of spectral values, and form a quasi-kontinu.Tiap kenampakn appearance of an object differ from one another Because of differences sma interval of pixel values are present, and Also Because of Different spatial patterns of the resulting impression. Dengan demikian, perubahan yang terjadi pada nilai piksel ataupun pada kesan pola spasialnya akan menghasilkan perubahn kenampakn citra tersebut. Thus, the changes in pixel values or the spatial pattern will produce the impression kenampakn perubahn image. Thus, changes in pixel values, or on the impression of spatial pattern will produce perubahn kenampakn image. Thus, changes in pixel values, or on the impression of spatial pattern will of Produce perubahn kenampakn image.
Inilah yang dijadikan prinsip dalam penajaman citra secara digital : bagaimana mengubah nilai piksel secara sistematis, sehingga menghasilkan efek kenampakn citra yang lebih ekspresif, sesuai dengan kebutuhan pengguna. This is taken as the principle of digital image enhancement: how to change pixel values in a systematic, resulting kenampakn effects more expressive image, in accordance with user needs. This is taken as the principle of digital image enhancement: how to change pixel values in a systematic, resulting kenampakn effects more expressive image, in accordance with user needs. This is taken as the principle of digital image enhancement: how to change pixel values in a systematic, more expressive effects kenampakn resulting image, the in accordance with user needs.
Penajaman kontras diterapkan untuk memperoleh kesan kontras citra yang lebih tinggi. Contrast enhancement is applied to get the impression that a higher contrast image. Contrast enhancement is applied to get the impression that a higher contrast image. Contrast enhancement is applied to get the impression That a higher contrast image. Hal ini dapat dilakukan dengan mentransformasikan seluruh nilai kecerahan. This can be done by transforming the entire value of brightness. This can be done by transforming the entire value of brightness. Can this be done by transforming the entire value of brightness. Hasilnya berupa citra dengan nilai maksimum baru yang lebih tinggi dari nilai maksimum awal, dan nilai minimum baru yang (pada umumnya) lebih rendah dari nilai minimum awal. The result is a new image with the maximum value that is higher than the initial maximum value, and the new minimum value (in general) is lower than the initial minimum value. The result is a new image with the maximum value that is higher than the initial maximum value, and the new minimum value (in general) is lower than the initial minimum value. The result is a new image with the maximum value That is higher than the initial maximum value, and the new minimum value (in general) is lower than the initial minimum value. Secara visual, hasil ini berupa citra baru yang variasi hitam – putihnya lebih menonjol, sehingga tampak lebih tajam dan memudahkan proses interpretasi. Visually, this results in the form of a new image of the black variety - white is more prominent, so that it looks sharper and simplify the process of interpretation. Visually, this results in the form of a new image of the black variety - white is more prominent, so that it looks sharper and simplify the process of interpretation. Visually, this results in the form of a new image of the black variety - white is more Prominent, so That it looks Sharper and Simplify the process of interpretation.
Algoritma penajaman kontras ini dapat dikelompokan menjadi dua : This contrast enhancement algorithms can be classified into two: This contrast enhancement algorithms can be classified into two: This contrast enhancement algorithms Can be classified into two:
1. 1. 1. 1. Perentangan kontras (Contrast stretching) Perentangan contrast (the Contrast stretching) Perentangan contrast (Contrast stretching) Perentangan contrast (the Contrast stretching)
2. 2. 2. 2. Ekualisasi histogram (Histogram equalization). Histogram equalization (histogram equalization). Histogram equalization (histogram equalization). Histogram equalization (histogram equalization).
II PERENTANGAN KONTRAS PERENTANGAN CONTRAST II PERENTANGAN PERENTANGAN CONTRAST Contrast
Kontras citra dapat dimanipulasi dengan merentangka nilai kecerahan pikselnya.perentangan yang efektif dapat dilakukan dengan memperhatikan bentuk histogramnya.Citra asli yang biasanya mempunyai julat nilai lebih sempit dari 0 – 255, perlu derentang sehingga kualitas citranya menjadi lebih lebih baik. Hasil perentangan ini adalah citra baru, yang bila digambarkan histogramnya berupa kurva yang lebih lebar. Image contrast can be manipulated with merentangka pikselnya.perentangan effective brightness value can be done by considering the form of original histogramnya.Citra which usually have a narrower julat value from 0-255, need derentang so that its image quality to be more better. Perentangan result is a new image which, when depicted histogramnya a wider curve. Image contrast can be manipulated with merentangka pikselnya.perentangan effective brightness value can be done by considering the form of original histogramnya.Citra which usually have a narrower julat value from 0-255, need derentang so that its image quality to be more better. Can be manipulated contrast image with brightness values merentangka effective pikselnya.perentangan Can be done by considering the form of original histogramnya.Citra Which usually have a narrower julat value from 0-255, need derentang That so its image quality to be more better. Perentangan result is a new image which, when depicted histogramnya a wider curve. Perentangan result is a new image Which, Pls histogramnya depicted a wider curve.
Ada beberapa cara untuk merentang kontras citra : There are several ways to stretch the image contrast: There are several ways to stretch the image contrast: There are Trust Airways to stretch the image contrast:
• Cara paling sederhana ialah dengan mengalikan citra tersebut • The simplest way is by multiplying the image • The simplest way is by multiplying the image • The simplest way is by multiplying the image
Misalnya dengan paktor pengali p =3, menghasilkan citra baru X'dengan julat 0-63. For example with p = 3 paktor multipliers, generate a new image X'dengan julat 0-63. For example with p = 3 paktor multipliers, generate a new image X'dengan julat 0-63. Pada pengaturan warna hitam-putih, citra baru ini akan tampak lebih kontras, karena julatnya makin lebarr. For example with p = 3 paktor multipliers, generate a new image julat X'dengan 0-63. In black and white setting, this new image will appear more contrast, because the more julatnya lebarr. In black and white setting, this new image will appear more contrast, because the more julatnya lebarr. Nilai maksimum lama, yaitu21, yang tam,pak gelap ditransformasikan menjadi nilai maksimum baru 63, yang tampak jauh lebih cerah.sedangkan nilai minimum dijaga tetap. In black and white setting, this new image will from Appear more contrast, Because the more julatnya lebarr. The maximum value a long time, yaitu21, which seemed, dark pack transformed into a new maximum value of 63, which seems a lot more cerah.sedangkan minimum value is kept. The maximum value of the old, yaitu21, which seemed, dark pack is transformed into a new maximum value of 63, which seems a lot more cerah.sedangkan minimum value is kept. The maximum value of the old, yaitu21, Which seemed, dark pack is transformed into a new maximum value of 63, Which Seems a lot more cerah.sedangkan minimum value is kept.
• Cara lain adalah suatu pengkondisian • Another method is a conditioning • Another method is a conditioning • Another method is a conditioning
Perentangan dilakukan pada julat diantara nilai maksimum dan minimum. Perentangan done on julat between the maximum and minimum. Perentangan done on julat between the maximum and minimum. Perentangan done on julat Between the maximum and minimum. Misalnya citra X (0…21) akan direntangkan menjadi cira X” (0…255), tetapi dengan mengambil nilai 3 sebagai nilai masukan minimum dan 19 sebagai nilai masukan maksimum.Dalam hal ini, nilai asli pad acitra x (0..21)yang <=3 akan menjadi 0 pada citra baru, dan nilai asli yang >=19 akan menjadi 255. For example the image of X (0 ... 21), will be flung into cira X "(0 ... 255), but by taking the value 3 as the minimum input value and 19 as an input value maksimum.Dalam this case, pad acitra original value x (0 .. 21 ) is <= 3 would be 0 on a new image, and the original value of> = 19 will become 255. For example the image of X (0 ... 21) will be flung into cira X "(0 ... 255), but by taking the value 3 as the minimum input value and 19 as an input value maksimum.Dalam this case, pad acitra original value x (0 .. 21 ) is <= 3 would be 0 on a new image, and the original value of> = 19 will become 255. Trasformasinya sebagai berikut : Trasformasinya as follows: For example the image of X (0 ... 21) will from be flung into cira X "(0 ... 255), but by taking the value 3 as the minimum input value and 19 as an input value maksimum.Dalam this case , pad acitra original value x (0 .. 21) is <= 3 would be 0 on a new image, and the original value of> = 19 will of changed from 255. Trasformasinya as follows: Trasformasinya Mutation
BVoutput =BV input – Bvmin =255 BVoutput = BV inputs - Bvmin = 255 BVoutput = BV inputs - Bvmin = 255 BVoutput = BV inputs - Bvmin = 255
BVmaks – BVmin BVmaks - BVmin BVmaks - BVmin BVmaks - BVmin
BVoutput adalah nilai kecerahan baru hasil transformasi, BVoutput adalah sembarang nilai kecerahan piksel pada citra yang menjadi masukan, BVmin adalah nilai kecerahn piksel minimum pada citra asli, dan BVmaks adalah nilai kecerahn maksimum piksel pada citra asli.Nilai koefisien 255 dimaksudkan untuk memperoleh citra baru dengan julat 0 – 255 (kecerahn maksimum). BVoutput is the new brightness value of the transformation, BVoutput are arbitrary brightness values of pixels in the image into the input, BVmin kecerahn pixels is the minimum value on the original image, and BVmaks is the maximum value of pixels in the image kecerahn asli.Nilai coefficient 255 is intended to obtain a new image with julat 0-255 (kecerahn maximum). BVoutput is the new brightness value of the transformation, BVoutput are arbitrary brightness values of pixels in the image into the input, BVmin kecerahn pixels is the minimum value on the original image, and BVmaks is the maximum value of pixels in the image kecerahn asli.Nilai coefficient 255 is intended to obtain a new image with julat 0-255 (kecerahn maximum). Apabila menghendaki nilai maksimum piksel hasil transformasi sebesar 200, maka nilai 255 itu pun dapat diganti dengan 200. BVoutput Is the new brightness value of the transformation, BVoutput are arbitrary brightness values of pixels in the image into the inputs, BVmin kecerahn pixels is the minimum value on the original image, and BVmaks is the maximum value of pixels in the image of the original kecerahn. coefficient value of 255 is intended to obtain a new image with julat 0-255 (kecerahn maximum). If want the maximum value of 200 pixels result of transformation, then the value of 255 it can be replaced with 200. If want the maximum value of 200 pixels result of transformation, then the value of 255 it can be replaced with 200. If want the maximum value of 200 pixels result of transformation, then the value of 255 Can it be replaced with 200. Pada persamaan ini,jika BV output ternyata negative maka nilai baru akan diatur menjadi sama dengan 0.Begitu pula apabila BV output >2555 maka nilai baru akan diatur men In this equation, if BV was negative then the output of the new value will be set as the same as if the BV also 0.Begitu output> 2555 then the new value will be arranged shortly In this equation, if BV was negative then the output of the new value will be set as the same as if the BV also 0.Begitu output> 2555 then the new value will be set shortly In this equation, if BV was negative then the output of the new value will from several sets as the Same as if the BV Also 0.Begitu output> 2555 then the new value shortly Will Be Arranged
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