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Digital Image Processing Interactive Tutorials

Section Overview:

Explore how the fundamental tools of digital image processing can be utilized to manipulate, rehabilitate, edit, resize, rotate, and store images captured with an optical microscope (or other digital image recording device). The interactive tutorials linked below each consider a specific algorithm or related series of algorithms that are useful for processing digital images.

  • Digital Image Sampling Frequency

    Explore and discover how variations in specimen sampling frequency affect the optical and electronic resolution of the final digital image from a confocal microscope in this interactive java tutorial.

  • Spatial Resolution

    Spatial resolution is a term that refers to the number of pixels utilized in construction of a digital image. Images having higher spatial resolution are composed with a greater number of pixels than those of lower resolution.

  • Gray-Level Resolution

    Gray-level resolution refers to the number of shades of gray utilized in preparing the image for display. Explore variations in digital image gray-level resolution, and how these variations affect the appearance of the image.

  • Contrast Manipulation in Digital Images

    Contrast refers to the amount of color or grayscale differentiation that exists between various image features in both analog and digital images. Explore how contrast variations affect the final appearance of the image.

  • Contrast Stretching & Histogram Normalization

    Explore how redistributing brightness values through application of contrast stretching and histogram normalization algorithms can rehabilitate digital images having poor contrast.

  • Grayscale Image Complement

    Grayscale image complement operations are useful for enhancing the visibility of subtle brightness variations among gray levels in regions of a digital image where fine details are obscured.

  • Image Averaging and Noise Removal

    Discover and learn more about various aspects of the image averaging algorithm, which is widely utilized for removing random noise from digital images in this featured interactive java tutorial.

  • Balancing Color in Digital Images

    Color balancing belongs to a class of digital image enhancement algorithms that are useful for correcting color casts in captured images. Explore the image enhancement technique of color balancing.

  • Levels Adjustment in Digital Images

    Explore the image enhancement technique of levels adjustment. The tutorial initializes with a randomly selected specimen image (captured in the microscope) appearing in the window entitled Specimen Image.

  • Output Look-Up Table Manipulation

    Discover how manipulation of the look-up table can be employed to alter various properties of a digital image, such as contrast and color values in this interacitive java tutorial.

  • Background Subtraction

    Application of a suitable background subtraction algorithm is a useful technique for correcting image defects that are associated with nonuniform brightness, often (but not always) attributed to uneven illumination in the microscope.

  • Line Intensity Scanning

    The line intensity scan function is a graphical tool that is useful for measuring intensity and contrast along a single horizontal or vertical row of pixels in digital images. Explore the line intensity scan technique for measuring intensity.

  • White and Black Balance

    The overall color of a digital image captured with an optical microscope is dependent on the spectrum of visible light wavelengths transmitted through or reflected by the specimen and the spectral content of the illuminator.

  • Gamma Correction

    The perceived brightness of a digital image captured with an optical microscope is dependent on the conditions of specimen illumination, and the sensitivity and linearity of the detector upon which the image was acquired.

Filtering Digital Images

  • Adjustment of Digital Image Sharpness

    Sharpness of a digital image refers to the degree of clarity in specimen detail. A lack of sharpness in digital images is often due to poor focus adjustment, vibration, or the specimen not being flat with respect to the imaging plane.

  • Convolution Kernels

    Many of the most powerful image processing algorithms rely upon a process known as convolution (or spatial convolution), which can be used to perform a wide variety of operations on digital images.

  • Convolution Kernel Mask Operation

    A powerful array of image-processing technologies utilize multipixel operations with convolution kernel masks, in which each output pixel is altered by contributions from a number of adjoining input pixels.

  • Median Filters for Digital Images

    Explore the removal of impulse noise from a digital image using the median filter, and how the application of this and related filtering techniques affect the final appearance of the filtered image.

  • Derivative Filters

    Derivative filters provide a quantitative measurement for the rate of change in pixel brightness information an image. When applied to an image, the resulting information about brightness change rates can be used to enhance contrast.

  • Fourier Transform Filtering Techniques

    Explore the Fourier transform as a tool for filtering digital images. The tutorial initializes with a randomly selected specimen image appearing in the left-hand window entitled Specimen Image.

  • Unsharp Mask Filtering

    The unsharp mask filter algorithm is an extremely versatile sharpening tool that improves the definition of fine detail by removing low-frequency spatial information from the original image.

  • Difference of Gaussians Edge Enhancement

    Edge enhancement algorithms employed in digital image processing often produce the unwanted side effect of increasing random noise. Explore the various gaussians algorithm to images captured in the microscope.

  • Spatial Averaging

    Numerous sources exist for noise that can seriously affect the quality of digital images captured in the microscope. Explore the benefits and consequences of spatial averaging as a method for removing noise from images.

Transformation of Digital Images

Creating and Manipulating Binary Digital Images

  • Binary Threshold Level Selection

    Discover and learn more about the use of various algorithms utilized in the methodology for choosing a single binary threshold level in this featured interactive java tutorial.

  • Binary Slicing of Digital Images

    Binary slicing is a technique that can be utilized to create a high-contrast binary image from a low-contrast grayscale image. Explore grayscale digital image contrast enhancement through the methods of binary slicing.

Compression of Digital Images

  • Color Reduction and Image Dithering

    Explore the compression of digital images using GIF algorithms, and how a lossy storage mechanism affects the final appearance of the image when interpolations are made from images having more than 256 colors.

  • JPEG Image Compression

    Discover compression of digital images with the JPEG algorithm, and how the lossy storage mechanism affects file size and the final image appearance in this interactive java tutorial.

Contributing Authors

Kenneth R. Spring - Scientific Consultant, Lusby, Maryland, 20657.

John C. Russ - Materials Science and Engineering Department, North Carolina State University, Raleigh, North Carolina, 27695.

Matthew Parry-Hill, Thomas J. Fellers, and Michael W. Davidson - National High Magnetic Field Laboratory, 1800 East Paul Dirac Dr., The Florida State University, Tallahassee, Florida, 32310.

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