Combined Use of a Bilateral and Median Filter to Suppress Gaussian Noise in Images

Project Code :TMMAIP413


This project aims to combine the strengths of bilateral and median filtering algorithms to achieve superior noise reduction in images corrupted by Gaussian noise


The paper is devoted to the problem of processing images with Gaussian noise. The method proposes a new way of denoising. This algorithm can effectively clean the image from noise and keep the edges of objects precise. 

The research was conducted in MATLAB R2019b, the quality of the proposed filter was tested using a peak signal-to-noise ratio and a structural similarity index. This method can de-noise images more efficiently in comparison with most existing methods. 

The proposed method de-noise better by 1-6 dB than comparable methods in the work. It can be used in applications, image processing, or as a precleaning program for an image recognition neural network

Keywords: Bilateral filtering, median filtering, peak signal-to-noise ratio, Gaussian noise, image denoising filter

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram


Software & Hardware Requirements:

Software: Matlab R2020a or above


Operating Systems:

  • Windows 10
  • Windows 7 Service Pack 1
  • Windows Server 2019
  • Windows Server 2016


Minimum: Any Intel or AMD x86-64 processor.

Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support.


Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation.

Recommended: An SSD is recommended A full installation of all MathWorks products may take up to 29 GB of disk space.


Minimum: 4 GB

Recommended: 8 GB

Learning Outcomes

·   Introduction to Matlab


·   How to start with MATLAB

·   About Matlab language

·   Matlab coding skills

·   About tools & libraries

·   Application Program Interface in Matlab

·   About Matlab desktop

·   How to use Matlab editor to create M-Files

·   Features of Matlab

·   Basics on Matlab

·   What is an Image/pixel?

·   About image formats

·   Introduction to Image Processing

·   How digital image is formed

·   Importing the image via image acquisition tools

·   Analyzing and manipulation of image.

·   Phases of image processing:

               o  Acquisition

               o  Image enhancement

               o  Image restoration

               o   Color image processing

               o  Image compression

               o   Morphological processing

               o   Segmentation etc.,

·   How to extend our work to another real time applications

·   Project development Skills

               o   Problem analyzing skills

               o   Problem solving skills

               o   Creativity and imaginary skills

               o   Programming skills

               o   Deployment

               o   Testing skills

               o   Debugging skills

               o   Project presentation skills

               o   Thesis writing skills

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