What is Image contrast enhancement ?
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 outcomes. We want to talk about image contrast in this section. The definition of contrast and its purposes, different types of Image 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?
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 Image contrast enhancement?
Image 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. Image contrast enhancement is one of the most important preprocessing steps in real-world machine vision systems and deep learning as a service. Image 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 lighting circumstances.
[elementor-template id="7488"]What is the purpose of Image contrast enhancement?
In many image processing applications based on machine learning as a service 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 consider contrast.
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 can perceive the world consistently despite the significant variations in lighting conditions.
Types of Image contrast enhancement methods
Image contrast enhancement online 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 Image 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.
Image 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.
Frequency Domain Contrast Control Powered by Deep Learning
The pioneering contrast enhancement techniques of unsharp masking derives from frequency domain knowledge where high pass filtering amplifies edges and fine details. Recent breakthroughs demonstrate deep learning’s role enhancing such frequency manipulation techniques.
One approach train convolutional neural network generating high pass filters outputs multiplied with originals images boost high frequency elements. Networks accept intensity-grams target style specifications adaptively construct optimized kernels features amplified accordingly different contexts. This allows bypassing laborious hand-coded filter creation previous methods forced.
In low pass filter generation, adversarial models provide alternative smoothing filter synthesis. Generator networks propose filter candidates trainable discriminator distinguishes synthetic smoothed images from reference ground truths learning coherent global structures without sacrificing pleasing local tone transitions. Successful trained generators export smoothers transferring natural aesthetics professional photographers emulate editing software tools automatically.
Deep Multi-Scale Contrast Enhancement Techniques
Decomposing images into different frequency bands and manipulating contrasts across scale pyramid levels allows enhancing details lossless manners high frequencies while preserving naturalness global structures low frequency domains optimally. Laplacian pyramid decomposition builds Gaussian stacks these principles enable multi-frequency contrast handling elegantly.
Recent implementations demonstrate using deep pyramid layering networks end-to-end trained progressively enhance images across cascading finer scales allows detail amplification magnitude and locations automatized by optimization losses functions incorporating perceptual metrics high level understood semantic features. Adversarial trained generators compete creating photorealistic edge aware filters applied intermediate pyramid layers synthesizing crisply contrasted detail renderings reviewers struggled differentiating from originals demonstrating capability matching human preference perceptual tolerances.
The Image contrast enhancement method at Saiwa
Saiwa supports a local Image contrast enhancement method known as Log Local Color Correction (LLCC). LLCC is an adaptive local Image 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 Image 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
Contrast enhancement in image processing Based on Nonlinear Space and Space Constraints
Remote sensing, environmental monitoring, pattern recognition and other fields all use this important and vital technology. Image cooling has become an essential part of its optimization with applications in remote sensing, environmental monitoring, pattern recognition and various fields. The study of classical finite mechanical systems is the focus of analytical dynamics. Evolutionary algorithms based on the theory of biological evolution, due to their specific optimization mechanisms, robustness and implicit parallelism, have recently been considered in most fields of optimization.
With the constant advancements of network technology, people are no longer limited to what can be achieved through communication with them and are more willing to learn different things in life through the network. Due to various factors such as photography angle, photography distance, photography conditions and imaging sensor, people receive several photos in the same area, the images obtained in the same area have changes such as rotation and zoom. Some remote sensing images contain poor visual effects such as insufficient contrast and blurred images. Some images have better overall visual effects but are not sharp enough for required information such as edge segments or line features. Some image bands contain a lot of large data, such as TM images; But the amount of information in each band has a certain correlation, which causes problems for further processing. With the aim of comprehensive analysis of different information in a scene image, image registration is proposed, which consists of combining different bands, different time phases, different imaging equipment or under different angles, different positions, different climates and others. Natural conditions in the same target area, two or more images are required to complete the geometric calibration process. In the process of image collection and transmission, it is susceptible to the influence of external environmental factors such as light intensity, transmission environment and imaging system, which reduces the quality of the image. The image created on the image surface of the camera by the spatial observation image is usually low in contrast, the gray level distribution is concentrated in the histogram, and the target is immersed in the complex background. To improve the visual effect and image quality, the image must be enhanced by contrast enhancement in image processing. As an effective digital media and as an effective information carrier, digital images are easily transmitted and distributed and uploaded and downloaded on the Internet.
