Description: The research paper Image Processing – Printing Technical CSE Abstract speaks about the domain of Image Processing- Printing specifically. Lately digital image processing has gained a tremendous footage. Images are processed to make them convey the inherent message and also to store, transmit and represent autonomous machine perception. Digital image processing is done by a digital computer that takes into consideration several elements of the image at the unit level, also considering the position and value. Digital image processing is not easy that it sounds to be. There are many external influences impeding the smooth flow of the process time and again. The images generated are analog in nature, processing converts the analog images to digital images via transfer functions. A transfer function maps the pixel values from the CCD to the available brightness values in the imaging software all the images so far have been plotted using linear transfer functions
What is a digital image: A digital image is picture which is divided into a grid of “pixels” (picture elements). Each pixel is defined by three numbers(x, y, z), and displayed on a computer screen.
The research paper suggests that every raw image has image and noise. A person involved in digital image processing keeps the image and removes the sound. A transfer function maps the pixel values from the CCD to the available brightness values in the imaging software all the images so far have been plotted using linear transfer functions. The research paper suggests that basically an image signal has varying frequencies.
Conclusion: The research paper concludes on a note suggesting what unsharp masking is. It says that unsharp masking consists of a 3 – step process. Firstly it is to make a copy of the image where each pixel is the average of the group of pixels surrounding it, so that the large features are not disturbed, but the small ones are blurred (this is the unsharp mask). Next, the pixel values of the original image are multiplied by a constant (“A”), and then the pixel values of the unsharp mask are subtracted from this one or more times (“B”). In this way, the large features are not changed by much, but the small ones are enhanced. And then finally a low – pass filter is applied to the result.
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