You’ve probably heard the word “contrast” if you’ve ever used a professional camera or a mobile phone for photography. One of the key terms a photographer deal with is this one. However, if we want to talk about something more specific, contrast and its adjustment are among the most significant elements that can greatly aid in how a photo is displayed. In fact, by being sufficiently knowledgeable about image contrast, we can easily adjust the desired photos and produce the desired outcome. We want to talk about image contrast in this section. The definition of contrast and its purposes, different types of contrast enhancement, and other topics that can be extremely helpful in our affairs are among the topics that are covered.
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.
