What is contrast enhancement?
What is contrast in the processing of an image?
The degree of color or grayscale differentiation between various image
features in both analog and digital images is defined as "contrast".
Images with higher contrast levels typically show more color or grayscale
variation than images with lower contrast levels. After an image has been
captured with a digital camera or converted to digital format by an
analog-to-digital converter, its brightness (also known as luminoucs
brightness) is measured in process as known as contrast enhancement.
What are the various types of contrast?
In photography, contrast is the visual proportion of various tones in an
image; this difference is what gives an image its texture, highlight, shadow,
color, and clarity. There are various types of contrast; the following are some
of the most important types:
Tonal disparity
High contrast
Low contrast
Color contrast
Tonal contrast
The distinction in brightness between the various components of the
image is referred to as tonal contrast. Tonal contrast can be used in both
color and grayscale images. For a medium-contrast image, you will be aiming for
a photo that includes tones from bright white to dark black and everything in
between, unless you are specifically trying to create a high- or low-contrast
image.
High contrast
Bright whites and deep blacks dominate high-contrast photographs, which
lack many mid-tones. High-contrast images can be produced in color or grayscale.
When photographing a subject or element that needs to stand out, such as a
silhouette, or when using vibrant colors against a gloomy, dark sky,
high-contrast images are ideal.
Low contrast
You will see a lot of gray tones rather than whites and blacks in
low-contrast photographs because they have very little tonal contrast. You will
notice colors that are closer in tone, such as yellow and orange, blue and
green, or red and purple, in color photographs with low contrast. Low-contrast
images lack a lot of shadows and highlights, giving them a dreamy feel instead
of details that stand out. For moody landscapes, portraits, or when you want to
highlight a scene with soft, warm tones, low-contrast photography is fantastic.
Color contrast
To produce an image with various levels of contrasting colors, color
contrast uses other contrast types (tonal, high, and low contrast). The tonal
value of each color on the color wheel is based on the idea that white is the
lightest color and black is the darkest. Yellow would be regarded as quite
light on a tonal value scale, whereas navy blue would have a darker value. More
contrast is produced when colors with different tonal values are placed next to
each other, whereas less contrast is produced by colors with similar tonal
values. Color contrast is extremely important in fields such as infrared
photography, which focuses on inverting colors for a dramatic effect.
What is contrast enhancement?
Contrast enhancement is one of the image processing techniques used to
increase the brightness difference between objects and their backgrounds as
well as the visibility of objects in the image. In other words, "contrast
enhancement" means pixel intensity modification and redistribution to
increase visibility. Contrast enhancement is one of the most important
pre-processing steps in real-world machine vision systems. Contrast enhancement
has a wide range of applications in industries ranging from medicine to
astronomy to manufacturing, in any case where image processing may occur under
sub-optimal lightening circumstances.
What is the purpose of contrast enhancement?
In many image processing applications where the subjective quality of
images is crucial for human interpretation, image enhancement techniques are
frequently used. Any subjective assessment of the quality of an image must take
contrast into consideration. The difference in luminance reflected from two
adjacent surfaces produces a contrast. In other words, contrast is the
difference in visual characteristics that helps an object stands out against
the background and other nearby objects. The contrast in visual perception is
determined by how an object differs from other objects in terms of color and
brightness. Because our visual system is more sensitive to contrast than
absolute luminance, we are able to perceive the world consistently despite the
significant variations in lighting conditions.
Types of contrast enhancement methods
Contrast enhancement is an important image enhancing research issue.
This section has described three methods for enhancing contrast.
Histogram equalization
HE, or histogram
equalization, is a very well-liked method for boosting an image's contrast. Its
fundamental concept entails mapping the gray levels according to the input gray
level probability distribution. This process flattens and stretches the dynamic
range of the image's histogram, increasing overall contrast. The traditional
histogram equalization method has the advantage of treating the image as a
whole. The technique works well in pictures where the foreground and background
are both dark or bright.
The technique can enhance x-ray views of bone structure and improve the
level of detail in overexposed or underexposed photos. HE has been used in a
variety of industries, including radar and medical image processing. The
simplicity and effectiveness of this technique are two of its main benefits.
The computation doesn't require a lot of processing power. It is effective at
drawing attention to the edges and borders between various objects, but it
might obscure smaller, smoother local details.
CLAHE
Contrast-Limited Adaptive Histogram Equalization (CLAHE) is an adaptive
contrast enhancement method. Adaptive histogram equalization serves as its
foundation. In addition to the standard Histogram Equalization technique is
adaptive histogram equalization. Instead of computing one histogram for the
entire image, this method computes multiple histograms, each corresponding to a
different tile of the image. The contrast of each tile is increased to
redistribute the image's pixel values.
Then, in order to remove artificially induced boundaries, the
neighboring tiles are combined using bilinear interpolation. To prevent
amplifying any noise that may be present in the image, the contrast can be
limited, especially in homogeneous areas. Therefore, using this technique will
enhance local contrast in an image and bring out more detail. Instead of
focusing on overall contrast, this approach emphasizes local contrast.
CLAHE is a method for preventing excessive amplification while
preserving the sub-blocks' high dynamic range. this technique, which was
developed for medical imaging, has been successfully used to enhance other
low-contrast images.
Morphological enhancement
The use of mathematical
morphology in image processing and analysis has spurred the development of a fresh
method for addressing various issues in this field. Concepts of shape from set
theory are the foundation of this strategy. In morphology, sets of objects are
considered to be present in an image. Mathematical morphology has emerged as a
natural strategy for a number of machine vision and recognition processes
because it allows for the identification of objects and objects' features
through their shape.
Contrast enhancement algorithms
There are two kinds of contrast enhancement algorithms: global and
local.
Global
Global algorithms assign the same output intensity value to all pixels
with the same input value, regardless of where they are in the image.
Local
Local algorithms adjust intensity based on the features of each pixel's
spatial neighborhood. It has been demonstrated that local algorithms provide
better results in general.
The contrast enhancement method at Saiwa
Saiwa supports a local contrast enhancement
method known as Log Local Color Correction (LLCC). LLCC is an adaptive local
contrast enhancement technique that increases contrast in both dark and bright
image regions (as opposed to methods that cannot deal with both types of
regions at the same time) and achieves better results with fewer halo
artifacts. This is accomplished through the use of a set of logarithmic tone
mappings that are locally applied to each pixel based on the brightness
characteristics of its surroundings.
The advantages of contrast enhancement at Saiwa
- A quick and accurate method
- Image contrast enhancement while preserving local
structure
- Fewer halo artifacts.
- Parameter adjustment to experience various
adjustment options
- Image aggregation of applying several images at
once
- View and save the generated images
- Exporting and archiving results on the user's
cloud or locally
- Saiwa team service customization using the
"Request for customization" option
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