Digital Image Processing Interactive Tutorials

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.
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Digital Image Processing Interactive Tutorials

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 entitledSpecimen 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.