Sometimes, we must increase picture resolution or require a specific image transformation. But, These kinds of activities are possible by using image transformation techniques. We know that technology is making advancements daily, and image processing is one of them. Whether we extract or enhance information from an image, image processing techniques are helpful.
It is also a fact that digital image processing is also popular for a reason. In addition, we use various techniques to increase picture resolution. Those techniques include image restoration, remote sensing, medical imaging, and image segmentation. We will explore different image transformation tips and techniques to learn about them in this article.
Table of Contents
What Are Image Transformation Techniques?
First, we must know that a wide range of approaches and practices exist for image processing. Moreover, we can apply these practices to produce various results in a vast range of image processing. Let’s learn about various image transformation techniques.
Image enhancement refers to the adjustment of digital images. The final results make the images more suitable for further analysis or display. For example, we brighten, sharpen, or remove noise from our images. Moreover, this technique also makes it easy to identify key features in digital images. We perform image enhancement through various methods.
Some most popular ways to increase resolution of an image are listed below:
- Decorrelation stretch
- Filtering with morphological operators
- Noise removal using a Wiener filter
- Histogram equalization
- Median filtering
- Linear contrast adjustment
- Contrast-limited adaptive histogram equalization (CLAHE)
- Unsharp mask filtering
We must know that this image transformation technique is subjective in nature.
We must know the fact that images are capable of deteriorating for so many reasons. Now let’s take an example. Suppose we took a picture with our grandparents a decade ago. In fact, The picture must be taken using an old camera. This may result in losing the original form of an image or making it hazy.
The image deterioration may occur due to some physical stress. Moreover, it can also happen due to additive noise or motion blur. So how will we restore the image? In this case, we use a degradation model that removes all the deterioration on the image. Furthermore, It works as a convolution with the linear shift-invariant.
Linear filtering is another image transformation process that we perform with the help of convolution. Besides, we get the value of the output pixel as a linear combination of the neighboring input pixels. Here, we add each element of an image to its local neighbor pixels. Kernel weights these pixels.
Independent Component Analysis
ICA helps us to separate a multivariate signal into its underlying component. We perform the ICA technique when extracting the desired component from multiple signals or components.
Object Detection Or Template Matching
When we want to search for the location of a template in a larger image, we use object detection. Moreover, we can also say that it is a very simple approach to object detection. What’s more, we apply the template image over the larger image. This is similar to the convolution process when finding the matching part.
It helps human users know the name of the object detected. For example, a vehicle, a plant, or anything else.
This is a very common technique that most people use in image transformation. We compress the file size of an image with the intention of preserving its quality. This technique is performed for many reasons, but some of them are:
- Storage saving
- To run image processing algorithms on different devices
- Reduction of bandwidth that helps to transmit the image.
When we want to change the appearance of an image, we perform image manipulation techniques. Besides, There are several reasons to perform image manipulation. For example,
- Removal of unwanted objects from an image.
- Adding a new object to an image.
- Graphic designers mostly use this technique to create film posters.
Neutral style transfer is an example of image manipulation that uses deep learning models.
Image Generation Technique (GAN)
Image generation technique refers to the synthesis of new images. In this process, we use Generative Adversarial networks. In addition, it helps us train deep-learning models on our image data. This is how we generate the same type of image data.
Moreover, we must know that two distinct models play a role in making GANs. These are:
The generator helps us to create fake images. Furthermore, the discriminator network helps to identify the differences between real and fake images.
It is a class of graphics and vision problems. In this process, we aim to learn the mapping between an output image and an input image. In this process, we use a training set of aligned image pairs. For example, we can draw a free-hand sketch of something and get a realistic view of that sketch.
What Are The Tricks Or Tips Of Image Transformations?
We follow some amazing tips and tricks when we want to perform an effective image transformation. Here are some most effective tips to know.
Note The Size & Format Of The Image
We must know that JPEGs are known for providing the best image quality and reduced size. But, Images having the same size as JPEG in GIF or PNG may appear distorted or gloomy.
Enable Both Humans And Search Engines To Access The Image.
We must add our images to the sitemaps if we want to gain Google’s attention. This is one of the successful strategies to get Google’s attention. Now, we want to point the search engines to our sitemap. What we need to do is to add a line to the robot.txt file. Moreover, we can also submit it using the Search Console.
Use Image CDN
CDN stands for content delivery network. CDNs handle the delivery, optimization, and transformation of visual content for website users. They help us to decrease the overall corporate expenses. As a result, we can save the workers hours.
What’s more, A large number of SMBs find CDNs reliable, and one such example is UploadCare.
What Is Filestack’s Image Transformation API?
Filestack’s image transformation API is helpful when we want to transform images in bulk. We just need to add conversion parameters to the URL of our image. The URL-based image transformation of Filestack API helps us to achieve unparalleled control and speed.
Users can resize and adjust the images when they upload them on Filestack. Moreover, You can use our image transformation tool with the following features:
- Auto Image conversion
- Easy Integration
- Modern UI
We know that visual data is widely available in this era of technology. But we need to perform image processing or image cropping techniques to transfer them over the internet. Moreover, It helps us in different situations like predictive modeling and information extraction.
Several advanced models have been developed to make image processing easier. Moreover, The above-mentioned techniques use those models.
What Are the Different Types of Image Processing Techniques?
Some popular image processing techniques are:
- Image compression
- The Image enhancement technique
- Image Restoration
- Independent Component Analysis
- lastly, Linear filtering.
What Is the Transformation of the Image?
Transformation of an image is performed for various purposes, such as:
- improving its quality,
- extracting useful information,
- and much more.
What Are the Four Different Types of Image Processing Methods?
- Object detection or template matching
- Image manipulation
- Image generation technique
- Image-to-image translation
What Are Image Enhancement Techniques?
Image enhancement refers to the modification of images for better displaying or viewing. And, We use features like image noise removal, brightening, and detection of key features.
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