Digital Image processing is a technique that is used to execute some functions on an image, in order to get an appreciated image or to abstract some essential details from it. It is a type of signal processing in which input is an image and output may be image or components/ features combined with that image. Nowadays, image processing is amidst the promptly growing technologies. Silicon Mentor are elaborated in establishing full custom or semi custom digital image processing projects to explain different need of Academic or university, for the achievement of M.Tech/ ME/ Masters or PhD degree. Digital image processing capabilities help in handling of the digital images by using mainframes. The three familiar aspects that all types of knowledge have to experience while using digital capability are pre-processing, improvement, and exhibit, details eradication.

Generally, Electrical & Electronics engineering, Computer science engineering, Information technology engineering, M.Tech, ME, Masters, or PhD candidates or research scholars have consequently opt this division for their instant research. Silicon Mentor helps in IEEE base paper selection for Digital Image Processing in area of the electrical engineering, computer science engineering, communication engineering and information technology. Our team of experts also offer project support and complete assistance for final year IEEE Digital Image Processing Projects.

Subsequent are the immense categories of Image processing work that is done by our team of professionals having great experience in writing. Have a look below!

  •         Classification
  •         Feature eradication
  •         Distribution
  •         Certification
  •         Pattern perception
  •         Projection
  •         Multi-scale signal scrutiny


Some of the latest and advanced techniques which are used in digital image processing by us are listed below:

  •         Pixelization
  •         Linear refining
  •         Principal factors research
  •         Autonomous fact search
  •         Hidden Markov models
  •         Anisotropic dispersion
  •         Limited differential equations
  •         Scale constant component translation
  •         Self-organizing maps
  •         Neural networks
  •         Distinct wavelet transforms


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