Steps involved in an image processing pipeline

Image processing is a set of techniques that involve manipulating and analyzing digital images to enhance their quality, extract information, or perform specific tasks. These tasks can be of various types, like recognizing objects, finding objects that are not visible, and sharpening and restoring the image.

Image processing is widely used in fields such as computer vision, medical imaging, remote sensing, and digital photography.

Let's take a look at the steps in the image processing pipeline in the diagram below.

Image acquisition

The first step in the image processing pipeline is image acquisition. This step involves capturing the raw image data, including pixel values and metadata, from cameras, scanners, or other sources and converting them into digital format. The resulting digital image is a matrix of pixel values, where each pixel is assigned a specific binary code or numeric value.

The digital image is typically stored in a specific file format, such as JPEG, PNG, TIFF, or RAW. These formats determine how the pixel values, metadata, and other information are encoded and stored within the file to preserve image quality or metadata.

Image preprocessing

After acquiring the image, it is preprocessed. Image preprocessing refers to a set of techniques and operations performed on images before they undergo further analysis. The goal of preprocessing is to enhance the quality of the image, remove unwanted artifacts, and prepare the image for subsequent tasks such as feature extraction, object recognition, or image analysis.

Some basic preprocessing techniques include resizing, scaling, rotating, cropping, and flipping an image. Next, we will look at a few techniques used in image preprocessing.

Image enhancement

It aims to improve the visual quality, clarity, and interpretability of an image by adjusting the brightness, contrast, and color balance of an image to make it more visually appealing or highlight specific features. Common enhancement techniques include histogram equalization, contrast stretching, gamma correction, and adaptive filtering.

Image restoration

Image restoration techniques are used to recover or restore degraded or damaged images to improve their quality, thereby removing the artifacts caused by noise, blurring, compression, or other factors. Examples of image restoration techniques include denoising, deblurring, super-resolution, and inpainting.

Image denoising

Image denoising is a technique used to reduce or remove noise from an image. Noise in an image can be introduced during image acquisition, transmission, or storage processes, and it can degrade image quality and affect subsequent analysis or processing tasks. Denoising can be done by spatial filters like mean, median, and the Gaussian filter or frequency domain filters.

Note: To gain insights about denoising and restoration techniques visit this link: how to denoise and restore an image in computer vision?

Image segmentation

Image segmentation involves dividing an image into meaningful and distinct regions or objects. Segmentation techniques can be based on various criteria, such as color, intensity, texture, or edge information. It is useful for object detection, tracking, and extracting region-specific information for further analysis. Common segmentation methods include thresholding, edge detection, region growing, clustering, and watershed segmentation.

Feature extraction

After processing the image, the useful features are extracted from the image. Feature extraction in image processing refers to the process of identifying and extracting meaningful and relevant information or features from an image. These features capture distinctive characteristics of the image that can be used for various tasks such as image recognition, object detection, image classification, and image retrieval. Some commonly used techniques for feature extraction in image processing are edge detection and texture analysis. Edge detection algorithms identify and highlight the boundaries or edges of objects in an image while texture analysis methods capture the spatial arrangement and statistical properties of texture patterns in an image.

Recognition or detection

The recognition or detection step in image processing involves identifying and classifying objects or patterns of interest within an image. This step utilizes the extracted features from the previous feature extraction step to make decisions about the presence, location, or characteristics of specific objects or classes.

Now that we have overviewed the steps in an image processing pipeline, let's model the steps in an example.

Example

Consider the scenario of an autonomous vehicle navigating through a busy city road. In this scenario, the image processing pipeline involves acquiring images from the vehicle's cameras, applying preprocessing techniques such as enhancing the images, segmenting the scene, detecting and recognizing objects like traffic signs, detecting lanes, and tracking obstacles. These steps collectively provide valuable information to the autonomous vehicle system, enabling it to safely navigate through the city street.

Conclusion

In conclusion, the image processing pipeline plays a vital role in various real-world applications, such as computer vision, medical imaging, autonomous vehicles, surveillance, and more. It enables us to extract meaningful information from images, make decisions based on visual data, and automate tasks that would otherwise be time-consuming to perform.