Audio Denoising Matlab Project with Source Code || Final Year Project || IEEE Based Project

ABSTRACT

                Audio noise reduction system is the system that is used to remove the noise from the audio signals. Audio noise reduction systems can be divided into two basic approaches. The first approach is the complementary type which involves compressing the audio signal in some well-defined manner before it is recorded (primarily on tape). The second approach is the single-ended or non-complementary type which utilizes techniques to reduce the noise level already present in the source material in essence a playback only noise reduction system. Noise reduction is the process of removing noise from a signal.Digital filters effectively reduce the unwanted higher or lower order frequency components in a speech signal. In this paper the speech enhancement is performed using different digital filters .In this real noisy environment is taken into consideration in the form of Gaussian noise. The Time domain as well as frequency domain representation of the signal spectra is performed using Fast Fourier transformation technique. MATLAB in built functions are used to carry out the simulation. Gaussian type noise is added using in-built function randn () and keyboard noise is added as a second speech file to the original speech signal. The filters remove the lower frequency components of noise and recover the original speech signal. It is also observed that keyboard noise is typical to remove as compared to Gaussian type but these filters performed well to get sharper spectra of original speech signal. Speech signal analysis is one of the important areas of research in multimedia applications. Discrete Wavelet technique is effectively reduces the unwanted higher or lower order frequency components in a speech signal. Wavelet-based algorithm for audio de-noising is worked out. We focused on audio signals corrupted with white Gaussian noise which is especially hard to remove because it is located in all frequencies.

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Mr. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +91-7276355704

Email: roshanphelonde@rediffmail.com

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Video Compression Matlab Project with Source Code || Final Year Project || IEEE Based Project

ABSTRACT

            The huge usage of digital multimedia via communications, wireless communications, Internet, Intranet and cellular mobile leads to incurable growth of data flow through these Media. The researchers go deep in developing efficient techniques in these fields such as compression of data, image and video. Recently, video compression techniques and their applications in many areas (educational, agriculture, medical …) cause this field to be one of the most interested fields. Wavelet transform is an efficient method that can be used to perform an efficient compression technique. This work deals with the developing of an efficient video compression approach based on frames  difference approaches that concentrated on the calculation of frame near distance (difference between frames). The selection of the meaningful frame depends on many factors such as compression performance, frame details, frame size and near distance between frames. Three different approaches are applied for removing the lowest frame difference. In this project videos are tested to insure the efficiency of this technique, in addition a good performance results has been obtained. 

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Contact:

Mr. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +91-7276355704

Email: roshanphelonde@rediffmail.com

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Matlab Project with Source Code Image Forgery Detection || Final Year Project || IEEE Based Project

ABSTRACT

             Image forgery means manipulation of digital image to conceal meaningful information of the image. The detection of forged image  is  driven  by  the  need  of  authenticity  and  to  maintain integrity of the image. A copy move forgery detection theme victimization adaptive  over  segmentation  and have  purpose feature matching is proposed. The proposed scheme integrates both block based   and   key point based   forgery   detection  methods. The proposed adaptive over segmentation algorithm segments  the  host  image  into  non over lapping  and  irregular blocks adaptively. Then, the feature points are extracted from  each  block  as  block  features,  and  the  block  features  are matched with one another to locate the labeled feature points; this   procedure can   approximately indicate   the   suspected forgery    regions.    To    detect    the    forgery regions more accurately, we propose the forgery region extraction algorithm which  replaces  the  features  point  with  small super  pixels  as feature  blocks  and  them  merges  the  neighboring  blocks  that have  similar  local color  features  into  the  feature  block  to generate    the    merged    regions. Finally,    it    applies    the morphological  operation  to  merged  regions  to  generate  the detected forgery regions. In cut paste image forgery detection, proposed   digital   image   forensic techniques capable   of detecting  global  and  local contrast  enhancement,  identifying the use of histogram equalization.

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Contact:

Mr. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +91-7276355704

Email: roshanphelonde@rediffmail.com

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Rain Removal using Image Processing Matlab Project with Source Code || Final Year Project || IEEE Based Project

ABSTRACT

                 The rain removal from an image in the rainy season is also a required task to identify the object in it. It is a challenging problem and has been recently investigate extensively. In this project the entropy maximization and background estimation based method is used for the rain removal. This method is based on single-image rain removal framework. The raindrops are greatly differing from the background, as the intensity of rain drops is higher the background. The entropy maximization is very much suitable for the rain removal. Experimental results express the efficacy of the rain removal by proposed algorithm is better than the method based on saturation and visibility features. The rain and non-rain parts in a single image are very closely mixed up and the identification of rain streaks is not an easy task. In this project, we compare a single-image rain streak removal based on morphological component analysis (MCA) by decomposition of rain streaks. The signal and image processing for the filtering and region specification are discussed in the previous works. In this method, a bilateral filter is applied for an image to decompose it into the low-frequency (LF) and high-frequency (HF) parts. The HF part is then decomposed into rain component and non-rain component by performing sparse coding and dictionary learning on MCA.

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Contact:

Mr. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +91-7276355704

Email: roshanphelonde@rediffmail.com

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Object Tracking Form Video Matlab Project with Source Code || Final Year Project || IEEE Based Project

ABSTRACT

               The ongoing research on object tracking in video sequences has attracted many researchers. Detecting the objects in the video and tracking its motion to identify its characteristics has been emerging as a demanding research area in the domain of image processing and computer vision. Most of the methods include object segmentation using background subtraction. The tracking strategies use different methodologies like Mean-shift, Kalman filter, Particle filter etc. The performance of the tracking methods vary with respect to background information. In this survey, we have discussed the feature descriptors that are used in tracking to describe the appearance of objects which are being tracked as well as object detection techniques. In this survey, we have classified the tracking methods into three groups, and a providing a detailed description of representative methods in each group, and find out their positive and negative aspects.

PROJECT OUTPUT

PROJECT VIDEO

Contact:

Mr. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +91-7276355704

Email: roshanphelonde@rediffmail.com

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