糖心vlog官网观看

Image processing involves using algorithms to modify digital images, enabling enhancement, restoration, compression, and analysis tasks. Some basic processes in image processing include contrast adjustments, blur, noise reduction, transformations, and compression for storage. An image can be anything鈥攆rom a photo, video, or X-ray鈥攂ut is a row of pixels that make a matrix. Digital image processing, which uses computers, focuses on algorithms instead of analog signal chain image processing. Image processing is a vital field closely related to image analysis and the development of computer vision.

Explore the types of image processing, how it works, and the many fields it touches. Also, discover its advantages and disadvantages, and how you can start in image processing.

Types of image processing

While image processing extends to more intensive computer processes, like image analysis and computer vision, every image requires three main types of low-level image processing so that you can view it on a computer:

• Image enhancement

• Image restoration

• Image compression

Image enhancement

In image enhancement, the goal is not to make destructive changes to the image but to ensure it has specific aesthetics that make it viewable for you. These include basic image adjustments like contrast, coloring, sharpness, noise reduction, and filtering. Image filtering creates denoising and sharpening by using low-pass and high-pass filters to improve visual appearance.

Image restoration

Image restoration is the process of restoring image quality lost during data acquisition. While image restoration uses some of the same filtering techniques as image enhancement, it restores images to a certain technical quality rather than creatively manipulating aspects of an image for aesthetic enjoyment. Some degradation may occur, including motion blur, information loss, out-of-focus shots, and excessive noise. Restoration aims to return this image to its 鈥渙riginal鈥� form, whereupon enhancement can occur.

Image compression

Image compression is the technical process of reducing image data to an efficient form. It maintains the form of the image while reducing the amount of bytes actually required to represent the image. Two kinds of image compression exist, and the best choice depends on your needs:

• Lossless compression: removes image redundancy without data loss in the image, best for archiving photos, videos, and medical images

• Lossy compression: involves data loss in the reconstructed image to save space and load faster

Various image compression algorithms exist to produce both lossless and lossy images. Lossless image formats include RAW and PNG files鈥攕ome lossy examples include JPEG and H.264 files.

How does image processing work

Image processing works by treating digital images as functions. Take a color image of red (R), green (G), and blue (B), for example. Each color is a value from 0 to 255, with the function looking like:

f(x,y)=r(x,y) g(x, y) b(x,y)

You can use a website like to input values from 0 to 255 for each amount of red, green, and blue to see what you get. Those three values make up every pixel in a digital color image.

The final function represents a vector. Image filtering manipulates an image by changing the value of the red, green, and blue inside the function. Meanwhile, image warping modifies the image by changing the positions of the pixels, such as stretching or distorting the image.

Image analysis and computer vision

Image analysis and computer vision represent mid-level and high-level image processing, respectively. Image analysis uses low-level image processing techniques, like edge detection, to find things like patterns, track features, track motion, perform image segmentation, and motion detection. It takes an image as input and feature attributes as output. These techniques are helpful for medical imaging, scientific applications, and industrial applications, like counting objects.

In turn, high-level image processing, such as computer vision, uses these attributes with machine learning, neural networks, or deep learning to interpret and analyze images in real time, similar to how humans perceive vision.

Digital vs. analog image processing

The main difference between digital and analog image processing is that digital image processing uses pixel matrices to represent images on a computer. On the other hand, analog image processing works only by manipulating two-dimensional electrical signals, significantly reducing the amount of processing you can do with them.

Who uses image processing?

Many industries use image processing for scientific, industrial, and artistic reasons. Some applications of image processing include:

• Biology

• 础蝉迟谤辞苍辞尘测听

• Radiology

• Bioinformatics

• Satellite images

• Commercial photo and video

Each industry uses image processing analysis to help them in their field. Below are a few examples of how they use image processing in their work.聽

Astronomers

Astronomers use image processing techniques to capture images of stars and galaxies through telescopes. They use a charge-coupled device (CCD) to capture images in greyscale and color information through filters. This enables them to process the image and reconstruct the color later using that information. For complex structures like galaxies, they use broadband filters to capture as much information as possible. An example is the Hubble Space Telescope, which uses seven broadband filters.

Health care professionals

Health care professionals use image processing with MRIs, CAT scans, and PET scans to help them provide patients with accurate diagnoses. The basic image processing techniques help them amplify certain aspects of an image to identify cancers, assist with surgeries, and perform image segmentation.

Filmmakers

Filmmakers use image processing when using digital cameras to acquire images. Cameras acquire raw images that they pre-process to correct any distortion or color to get a 鈥渘ormal鈥� looking image. This allows them to creatively manipulate images to create a desired aesthetic look through color-grading.

Filmmakers can then add visual effects (VFX) to the images. Eventually, they will export and compress the image in various forms, depending on what delivery format is needed.聽Filmmakers use image restoration techniques to restore old films to their 鈥渙riginal state鈥� as closely as possible.

Pros and cons of image processing

Image processing, specifically digital image processing, has many advantages. It gives its practitioners many techniques to manipulate, analyze, and study images. It has a few disadvantages that are mostly related to the hardware and storage requirements. Explore those advantages and challenges below.

Advantages

The advantages of image processing lie in the wide range of techniques available to manipulate digital images. This includes contrast, color, size, noise, sharpness, distortion, compression, and quality. Other advantages include:

• Ability to enhance hard-to-see details or failures in the acquisition process

• Various color processing models available for both scientists and creatives

• Techniques and algorithms for compressing images to save storage space while maintaining quality

• Ability to use segmentation to analyze specific components of an image separately

• Low-level processing to prepare images for a processing pipeline that includes feature recognition, classification, and the application of machine learning and neural networks in computer vision

Challenges

Image processing challenges arise when considering the hardware, software, and storage required to work with images in this manner. Consider these challenges in image processing:

• Specialized software is needed to work with and process images.

• Specialized software also requires a dedicated graphics processing unit (GPU) in your computer to work efficiently with images.

• Images take up lots of storage, especially when working with video.

How to get started in image processing

To start basic image processing by manipulating images using a graphic user interface (GUI), try an app like Photoshop or Affinity Photo. This will allow you to get a feel for the adjustments you can make to an image.

However, if you鈥檙e looking to build skills in image processing from a computer science or software development perspective, consider using the Python library Sci-Kit Image. It contains an open-source library of algorithms for image processing in Python, including exposure change, color manipulation, edge detection, upscaling, feature detection, and segmentation.

Advanced skills in image processing include the knowledge of linear algebra, probability, statistics, and Python programming, especially for those looking to learn more about computer vision.

Getting started in image processing with 糖心vlog官网观看

Image processing is the first step in the pipeline of image analysis and eventually computer vision. Are you looking to learn more about image processing? Try the Image Processing for Engineering and Science Specialization from MathWorks on 糖心vlog官网观看 to gain basic skills in the field aimed at engineers and scientists who work with images.聽