T490

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Image Enhancement: is the process of mainpulating am image so that the result is more suitable than the original. - The Enhancement techniques:

Types of neighborhood:

1- Intensity transformation. - Neighborhood of size 1×1 تكون للصور ذات الحجم 1×1 من اجل التخلص من يعض العيوب مثلا بعد تكبير الصورة نجد بعض التشويش فنستخدم نفس المعادلة الماضية ولكن نعوضها بطريقة أخرى S = T (r)

Two approach of intensity transformation

Basic intensirty transformation

3- Power n'th power and n'th root S = Cr ^ y y is Gammaتأثيرها: كلما زادت قيمتها تغمق وكلما قلت قيمتها تفتح اكثر if y < 1 نفتح الصورة if y > 1 نغمق الصورة if y = 1 so it is entity C = r = 1

2- log arithmic log and invers log S = C log (1 + r) C is a constant r >= 0 used for expand the dark color

5- The power-law transformation are much more versatile for this purpose then the log transformation.

4- The opposit is true of the invers log.

3- Used to expand the values of dark pixels in an image while compressing the higher-level values. توسع البكسل الغامقة في حين تضغط القيم في مستوى أعلى

2- THe opposit is true of higher values of input levels. أي ان العكس صحيح ان القيم تكون اعلى في مستويات المدهلات

1- It maps a narrow range of low gray-level in the input image into a wider range of output level.

1- Linear negative and identity S = L - 1 - r

Benefits

2- Enhances white or gray detail embedded in dark regions يوضح لنا تفاصيل الصورة خصوصا درجات الأبيض والرمادي

1- Produce an equivalent of a photographic negative

Piecewise linear transformation functiions

Types

3- Bit-plane slicing

2- Gray-Level slicing - for intensity-level slicing

1- Constrast streching, thresholding

Reasons of this: 1- poor illumination 2- lack of dynamic range in the imaging sensor 3- wrong setting of alens aperture during image aquistion

why or what the idea behined constrast stretching: To incrase the dynamic range of the gray levels by making the pixels with dark intensities darker and the bright onse more brighter (higher constrast image)

its functions: - crate a binary image - convert pixels with dark intensities into black and the bright pixels into white. pixels above the threshold is considred bright and below it is considred dark

after constrast streching (r1, s1) = (r min, 0 ), (r2, s2), = (r max, L-1)

Advantages

2- Some practical implementation can be formulated only as piecewise functions.

1- The form of piecewise is complex

Disadvantages

1- Their specification requires more user input تتطلب من المستخدم مدخلات كثيرة

2- Spatial filter Called mask, kernal, tamplate or window نستعمل هذه الطريقة للصور الأكبر حجما

1- On spatial domain: - Direct mainpulation of pixels in the image plan. - Refers to the aggregate of pixles composing an image. - g(x,y) = T[ f(x,y) ] - g is the output, f is th input, T is the operation.

The mask or neighborhood: T is applied at each location (x,y) to produce the output g at that location.

2- On frequency domain.