Image enhancement is the process of adjusting digital images so that the results are more suitable for display or further image analysis.
Understanding Image Enhancement
In essence, image enhancement involves manipulating a digital image to improve its visual appearance or to prepare it for subsequent processing tasks. The goal is to make certain features or aspects of the image more prominent or easier to interpret, depending on the intended application.
Why is Image Enhancement Performed?
According to the definition, image enhancement is done to make images:
- More suitable for display: This often involves making images look better for human viewing, such as adjusting brightness, contrast, or color balance.
- More suitable for further image analysis: This could involve processes that help automated systems or human analysts extract information more easily, such as making edges sharper or removing distractions like noise.
Common Image Enhancement Techniques
Various techniques fall under the umbrella of image enhancement, each targeting different aspects of the image quality or content. Based on the provided reference, examples include:
- Noise Removal: Eliminating unwanted random variations in pixel values that can obscure details.
- Sharpening: Increasing the clarity of edges and details within the image.
- Brightening: Adjusting the overall luminance of the image to make it lighter.
These adjustments are made to facilitate easier identification of key features within the image, whether for a viewer or for an algorithm.
How Techniques Achieve the Goal
Each technique achieves the goal of making features easier to identify in different ways:
- Noise Removal: By smoothing out random variations, true image features (like edges or textures) become clearer and less likely to be confused with noise.
- Sharpening: By increasing contrast along edges, boundaries between objects or areas become more distinct.
- Brightening: In images that are too dark, increasing brightness can reveal details that were previously hidden in shadows.
These processes are fundamental in fields ranging from photography and graphic design to medical imaging, satellite analysis, and computer vision, where clarity and feature visibility are crucial.
