Image enhancement

Years ago, I put a post up noting definitions from McAndrew's book on image processing. I still get the odd question about specifics within those generic definitions. So here's a little deeper look at enhancement operations.

Anil K. Jain (Fundamentals of Digital Image Processing, 1989) defined image enhancement as "accentuation, or sharpening, of image features such as edges, boundaries, or contrast to make a graphic display more useful for display or analysis." Jain goes on to note that the enhancement process doesn't increase the inherent information content in the data. It's like I've always said, we don't change content or context - we just help the trier of fact see and correctly interpret what's there in the image (or video).

One of the issues where we tend to have problems is quantifying the criterion for enhancement. As such, a large number of enhancement techniques are empirical and require interactive procedures in order to obtain satisfactory results. In essence, enhancement is hands-on and participatory. Analysts must make judgements as to what to do, in what order, and at what strength.

With this in mind, I want to spend some time exploring enhancement operations. I'll start today with a bit of a road map.

Enhancement's general operations:

• Point Operations
• Spacial Operations
• Transform Operations
• Pseudocolouring

Point operations include:

• Contrast Stretching
• Noise Clipping
• Window Slicing
• Histogram Modeling

Spacial Operations include:

• Noise Smoothing
• Medial Filtering
• Unsharp Masking
• Low-pass, High-pass, and Bandpass filtering
• Zooming

Transform Operations:

• Linear Filtering
• Root Filtering
• Homomorphic filtering

Pseudocolouring:

• False Colouring
• Pseudocolouring

With these categories and operations in mind, I'll spend some time explaining them and putting them in context in future posts.