Cameras, webcams, and optical sensors are very common these days, so it’s no wonder we’re getting a lot of image and video data. Even though displaying information in image form is always easier, processing such big imagery data remains challenging.

However, AI and machine learning are doing wonders in the image-processing realm. They together give rise to a highly efficient and robust system where ML models hasten image and video classification, segmentation, and recognition.

In this blog, we’ll explore image processing techniques in-depth and examine various technologies we can use to develop state-of-the-art algorithms.

What is Image Processing?

In simple words:

Image processing involves converting an image to a digital format and performing certain functions to look better or have more information.

Also, we can define image processing (IP) as the technical analysis of an image using complex algorithms. A picture is fed into the algorithm as an input, resulting in a piece of useful information returned as an output. It’s a key preprocessing step in many applications, such as face recognition, object detection, and image compression. Also, a report predicts that By 2021, the image processing industry will generate USD 38.9 billion.

Since IP is used to enhance an existing image, also, sift out crucial details from it, it holds a special palace in various Deep Learning-based Computer Vision applications. Here, such preprocessing can amazingly boost the performance of a model.

Why is Image Processing Required? A Quick rundown:

  • Image processing allows you to represent data visually — so anyone can understand it. Moreover, with IP, you can give a visual form to invisible objects.
  • Convert the image in any desirable format (improved, X-Ray, photo negative, etc.).
  • Helps in measuring the number of objects in the image.
  • Pattern recognition helps significantly classify objects in the image, hitting on their position and offering a better understanding of the scene.
  • Improves the image quality with image sharpening and restoration.
  • Images can be stored and retrieved easily.

Phases of Image Processing

The basic digital image processing steps involve:

Phases of Image Processing

1. Image Acquisition

Image acquisition involves a camera that captures the images and converts them into a digitized form (in case the camera doesn’t support auto-digitized output) using an analog-to-digital converter for further processing.

2. Image Enhancement

As its name says, it enhances the captured image according to the requirements. Mainly, It focuses on highlighting the unseen details of an image, such as adjusting the contrast or brightness so that you can get a good overall image.

3. Image Restoration

Here, the appearance of an image is improved to get a clear vision via a probabilistic and mathematical model. Image restoration aims to correct or reverse the effects of various defects that can degrade an image, including but not limited to motion blur, noise, and misfocus resulting from camera settings.

4. Color Image Processing

In this step, the processing of colored images (16-bit RGB or RGBA images) takes place. For example, performing color correction or color modeling in images.

5. Multi-resolution Processing

Multi-resolution processing enables the representation of an image in more than one resolution or scale. It helps detect features that might be overlooked at one resolution and easily spotted in another.

6. Image Compression and Decompression

Compressing an image enables you to minimize its size in bytes without degrading its quality below an acceptable threshold. Image compression gives you more memory space and stores a lot of images without any hassle. Moreover, image decompression is done depending on the need.

7. Morphological Processing

Morphology is basically a broad set of image processing operations that process images based on shapes. Its basic operations include binary convolution and correlation based on logical operations rather than arithmetic operations. In Morphological processing, pixels are added or removed from the image, hence defining the object structure and shape in the image.

8. Image Segmentation

Image segmentation is a method that breaks downs a digital image into various subgroups commonly known as Image segments. These segments reduce the image complexity and make image processing more simple and easier. Segmentation, in easy words, is assigning labels to image pixels.

9. Representation and Description

Image segmentation further leads to this step. Here, the representation deals with the image’s characteristics and regional properties. It decides whether the segmented region should be selected as a boundary or the region as a whole. Whereas, Description extracts quantitative information that helps differentiate one class of objects from the other.

10. Object Detection and Recognition

After performing object segmentation, representation, and Description steps, the automated system is required to label the objects. This labeling lets users know what object has been detected. For example, a car or animal, etc.

The Advanced Image Processing Techniques You Need to Know

Image processing is a broad but complex field, however. It contains a wide variety of algorithms and techniques to achieve perfect results. This section will focus on some of the most advanced image processing techniques for machine learning and their working.

Image Restoration

Images could be damaged for various reasons. Hard copies of pictures taken with old instant cameras often get scratched when scanned. Also, the images were captured back when cloud storage wasn’t an option. Also, there could be blurred and noisy images because of motion blur, noise, etc., caused by environmental effects and camera misfocus. All such images in a damaged state can be restored via the image restoration technique.

Here are some image-processing techniques for restoring images:

  • Reverse Filter

This technique involves viewing an image degrader while assuming a conventional function. You’ll learn that restoration works great when there is no noise. On the other hand, its performance gets slows when the image carries a noise.

  • Weiner Filters

In this part, you apply a weiner filter for restoring the images. It gives you the best possible balance between inverse and de-noising filtering. The output is generally better than inverse filtering done directly.

  • Wavelet Restoration

Helps in recovering the image.

Blind Deconvolution

Restoring an image is quite challenging when you know nothing about it. In such a situation, Blind deconvolution algorithms work well. It converts the image and the point-spread function (PSF) altogether.

Image Restoration is pretty fascinating because advanced techniques in such areas can potentially restore damaged historical documents. Also, torn documents may be restored using powerful Deep Learning algorithms.

Linear Filtering

As its name suggests, Linear filtering is a filtering method with the output pixel’s value as linear combinations of the neighboring input pixels. It can be done with convolution—for example, mean/average filters or Gaussian filtering. Non-linear filtering cannot be done with convolution or Fourier multiplication.

Independent Component Analysis (ICA)

Independent Component Analysis (ICA) is a statistical technique that decomposes a complex dataset into independent sub-parts. Since it develops from blind source separation, it tries to transform an observed multidimensional vector into statistically independent components much as possible. Machine Learning and statistics are two areas where ICA excels.

Template Matching

Template matching is one of the image processing techniques that finds and detects small parts of an image that match a template image. This technique is implemented through two-dimensional convolution. Its application includes quality control in manufacturing, navigation of mobile robots, and image edge detection.

Image Generation Technique

You can use Generative Adversarial Networks (GANs) to generate the same type of image data by training a deep learning model.GANs are made of two distinct models:

  • Generator
  • Discriminator

The generator’s job is to generate fake images, whereas the discriminator tries to differentiate between fake and real images. Generators try to outsmart discriminators by making better fake images, and discriminators try to improve their ability to distinguish between fake and real images.

Examples of Image Processing Enhancing Visual Quality

Image processing techniques typically focus on improving the quality of images or extracting helpful information from them. Here are some examples of image processing:

  • Image Filtering

It involves applying various filters to an image to remove noise, blur or sharpen the image, or enhance certain features.

  • Image Segmentation

It divides an image into different regions based on its features or properties.

  • Image Compression

Reducing the size of an image without losing significant information and image compression save storage space while speeding up transmission over a network.

  • Image Recognition

Leveraging computer algorithms, it recognizes objects, people, or image patterns, such as facial recognition, object detection, or optical character recognition (OCR).

  • Image Restoration

It restores or improves the quality of a degraded or damaged image, such as removing scratches, stains, or other defects.

  • Image Analysis

It involves extracting information from an image, such as measuring the size or shape of objects, identifying patterns or trends, or detecting anomalies.

  • Image Synthesis

Image synthesis creates new images from existing ones, such as generating realistic images from textual descriptions or creating artistic renderings of photos.

Examples of Image Processing

Image Processing Applications: Enhancing Digital Imagery and Beyond

Since image processing is a broad field with many diverse applications, it continues to play an important role in many areas of technology and industry, including:

  • Medical Imaging

Image processing is widely used in medical imaging to analyze and interpret images from various medical imaging modalities like MRI, CT, X-ray, ultrasound, and more.

  • Surveillance

Surveillance systems also use image processing to detect and track objects or individuals in real time.

  • Robotics

Image processing is used in robotics to help robots interpret and react to visual stimuli.

  • Digital Photography

Image processing is used in digital cameras to enhance image quality and reduce noise.

  • Gaming

Image processing is used in gaming to create realistic 3D graphics and to track user movement for motion-controlled games.

  • Remote Sensing

Image processing analyzes and interprets images captured from remote sensing platforms, such as satellite images.

  • Industrial Automation

Image processing is used in manufacturing to inspect products for quality control and to automate production lines.

Ready to take your image processing to the next level? Let our AI experts show you how.

Wrapping up

Image processing techniques are helping to create more accurate and efficient models for object recognition, image classification, and other image-related tasks. In addition, with the rise of deep learning and neural networks, image processing techniques are becoming more sophisticated, allowing for more complex image analyses, such as segmentation, object detection, and image restoration. With these advancements, we can expect image-processing techniques to continue playing a critical role in various fields.

The future of image processing techniques in the machine learning realm is exciting and promising. With the increasing availability of big data, more advanced algorithms, and more powerful computing hardware, image-processing techniques are expected to continue revolutionizing the image-processing landscape.