As the name suggests, processing an image entails a number of steps before we reach our goal. The end result may be an image or a feature that is similar to that image. Additional research and decision-making can be done using this information. Below are the best image processing tools used in machine learning. 


A popular library that is simple to use is multi-platform. It flawlessly integrates with C++ and Python and provides all the essential methods and algorithms needed to complete a few image and video processing tasks (Salvi et al., 2021). 


Since it allows for rapid prototyping, Matlab is a great tool for creating image processing systems and is extensively used in research. Another interesting point is how much shorter Matlab code is than C++ code, making it simpler to read and troubleshoot. It addresses errors prior to execution by offering a number of options to expedite the process. 


NVIDIA, which is fast, highly efficient, and easy to program, serves as the foundation for parallel computing. It gives outstanding performance by utilizing GPU power. Incorporated into its toolset is the NVIDIA Performance Primitives library, which offers a variety of image, signal, and video processing techniques (Abdulrahman et al., 2021). 


The most used deep learning and machine learning library currently in use is this one. It quickly gained popularity and surpassed competing libraries due to the ease of use of the API.


A system for creating computer vision applications is called SimpleCV. It provides access to several computer vision technologies on any OpenCV, pygame, and other platforms. You need this program if all you need to do is finish the task and you don’t want to learn all the specifics of image processing. SimpleCV is the best option if rapid prototyping is required. 

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