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 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

• Geometric Transformation: Interpolation and Image Rotation

  Explore the properties of geometric transformation, and how the algorithms can influence the final appearance of the transformed image.

• Geometric Transformation: Pan, Scroll, Rotate, Flip, Scale, and Zoom

  Discover and learn more about the effects of panning, scrolling, rotating, flipping, scaling, and zooming operations on a variety of digital images in this interactive java tutorial.

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.