Image enhancement refers to the process of improving the visual quality of an image, making it more suitable for analysis or presentation. This can involve adjusting various aspects of an image, such as brightness, contrast, sharpness, and color balance. The goal of image enhancement is to highlight important features, improve visibility, and make the image more visually appealing. Here are some common techniques and concepts in image enhancement:

1. Contrast Enhancement:

  • Histogram Equalization: Adjusts the distribution of pixel intensities to cover the entire available range, enhancing contrast.
  • Contrast Stretching: Linearly scales the intensity values to utilize the full dynamic range.

2. Brightness Adjustment:

  • Gamma Correction: Modifies the gamma value to control the overall brightness of the image.

3. Sharpness Enhancement:

  • Unsharp Masking: Involves subtracting a blurred version of the image from the original to enhance edges.
  • High-pass Filtering: Emphasizes high-frequency components to enhance details.

4. Color Correction:

  • Color Balance: Adjusts the intensities of the red, green, and blue channels to achieve a more natural color appearance.
  • Saturation Enhancement: Increases the vividness of colors in the image.

5. Noise Reduction:

  • Smoothing Filters: Applies filters to reduce high-frequency noise while smoothing the image.
  • Median Filtering: Replaces each pixel with the median value in its neighborhood, effective for salt-and-pepper noise.

6. Histogram Modification:

  • Histogram Matching: Adjusts the image’s histogram to match a specified reference histogram.

7. Dynamic Range Adjustment:

  • HDR Imaging (High Dynamic Range): Combines multiple exposures of the same scene to capture a wider dynamic range.

8. Edge Enhancement:

  • Laplacian Filter: Emphasizes high-frequency components, enhancing edges.
  • Gradient Filters: Compute the gradient of the image to highlight edges.

9. Detail Enhancement:

  • Enhancement Filters: Apply filters that boost certain frequencies to bring out specific image details.

10. Super-Resolution:

  • Image Upscaling: Uses algorithms to increase the resolution and clarity of an image.

Applications of Image Enhancement:

  1. Medical Imaging: Improving visibility of structures in medical images for diagnosis.
  2. Satellite Imaging: Enhancing satellite images for better analysis of geographical features.
  3. Forensic Analysis: Enhancing details in surveillance footage for better identification.
  4. Art and Photography: Adjusting images to achieve desired artistic effects.
  5. Computer Vision: Pre-processing images to improve the performance of computer vision algorithms.

Image enhancement is a crucial step in various fields, and the choice of techniques depends on the specific requirements of the application and the characteristics of the images being processed. Advanced image enhancement methods often involve a combination of multiple techniques to achieve the desired results.

Image enhancement is among the simplest and most appealing areas of digital image processing. Image enhancement is used to bring out details that are obscured(keep from being seen) or simply to highlight certain features of interest in image. Image enhancement is a very subjective area of image processing

Image enhancement approaches fall into two broad categories

  • spatial domain methods
  • frequency domain methods

spatial domain methods

In image enhancement spatial domain refers to the image plane itself, and approaches in this category are based on direct manipulation of pixels in an image.

frequency domain methods

in image enhancement frequency domain methods techniques are based on modifying the Fourier transform of an image. Enhancing an image provides better contrast and a more detailed image as compare to non enhanced image

Spatial domain processes syntax

g(x,y) = T[f(x,y)]

where f(x, y) is the input image, g(x, y) is the processed image, and T is an operator on f, defined over some neighborhood of (x, y). The principal approach in defining a neighborhood about a point (x, y) is to use a square or rectangular subimage area centered at (x, y), The center of the subimage is moved from pixel to pixel starting, say, at the top left corner. The operator T is applied at each location (x, y) to yield the output, g, at that location. The process utilizes only the pixels in the area of the image spanned by the neighborhood.


  • Imagenegative
  • Logtransformations
  • Power lawtransformations
  • Piecewise-Linear transformationfunctions


Linear transformation includes simple identity and negative transformation.

Identity transition

In this transition, each value of the input image is directly mapped to the other value of the output image. That results in the same input image and output image. Hence is called identity transformation.


Negative transformation, which is invert of identity transformation. In negative transformation, each value of the input image is subtracted from the L-1 and mapped onto the output image

L is the largest gray level consists in the image


The imagenegativewithgraylevelvalue inthe rangeof[0,L-1]isobtained bynegative transformation given by S = T(r)

Where T(r)=S = L -1 – r

Where r= gray level value at pixel (x,y)


.Logarithmic transformation further contains two type of transformation. Log transformation and inverse log transformation.


The log transformations can be defined by this formula

s = c log(r + 1).

Where s and r are the pixel values of the output and the input image and c is a constant. The value 1 is added to each of the pixel value of the input image because if there is a pixel intensity of 0 in the image, then log (0) is equal to infinity

During log transformation, the dark pixels in an image are expanded as compare to the higher pixel values. The higher pixel values are kind of compressed in log transformation.


There are further two transformation is power law transformations, that include nth power and nth root transformation. These transformations can be given by the expression:


This symbol γ is called gamma, due to which this transformation is also known as gamma transformation

  • γ > 1 compresses dark values (Expands bright values)
  • γ <1(similarto Logtransformation) Expands dark values Compresses brightvalues

When C = γ = 1 , it reduces to identity transformation

Piecewise-Linear Transformation Functions:

  • Contrast stretching:One of the simplest piecewise linear functions is a contrast-stretching transformation. Low-contrast images can result from poor illumination, lack of dynamic range in the imaging sensor, or even wrong setting of a lens aperture during image acquisition

S= T(r )

Gray-level slicing:

Highlighting a specific range of gray levels in an image often is desired. Applications include enhancing features such as masses of water in satellite imagery and enhancing flaws in X-ray images. There are several ways of doing level slicing, but most of them are variations of two basic themes. One approach is to display a high value for all gray levels in the range of interest and a low value for all other gray levels.

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