Noise Minimization In Images Using Dual-Tree Complex Wavelet Transform

Vol.1  No.3

Year : 2014

Issue : Jul-Sep

Title :  Noise Minimization In Images Using Dual-Tree Complex Wavelet Transform

Author Name :  ARUNKUMAR M, leela rani

Synopsis :

This paper presents a novel way to reduce noise introduced by different image enhancement techniques. As the human visual system is highly sensitive to change in brightness, the proposed method is applied to the luma channel of both the non-enhanced and enhanced image. The basic assumption is that the non-enhanced image is either free of noise or noise is present but not perceivable. In order to avoid inappropriate assumptions on the statistical characteristics of noise, a different one is proposed. Also, it gives the importance of directional content in human vision and the analysis is performed through the Dual-Tree Complex Wavelet Transform (DTCWT). Compared to discrete wavelet transform, the DTCWT provides distinction of data directionality in the transform space. The standard deviation for each level of the transform of an non-enhanced image coefficients is computed and normalized across the six orientations of the DTWCT. The normalized said map is then used to shrink the coefficients of the enhanced image. The shrunk coefficients of the enhanced image are mixed according to data directionality, with the coefficients of non-enhanced image. Finally, the inverse transform provides the noise-reduced version. The proposed one thoroughly reduces the noise introduced by the enhancement methods and produces better improvement in PSNR of the image. In order to confirm the validity of the proposed method, a through numerical analysis of the results has been done.

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New Lifting Wavelets Based Medical Image Compression With Entropy Coding

Vol.1  No.3

Year : 2014

Issue : Jul-Sep

Title :  New Lifting Wavelets Based Medical Image Compression With Entropy Coding

Author Name :  Arikatla Hazarathaiah, B. Prabhakara Rao

Synopsis :

The main limitations on any communication system are “channel bandwidth” and “noise”. This paper deals with the data rate which is related to channel bandwidth. The real time images from any source will occupy large amount of space on a storage device. Hence it consumes maximum bandwidth for transmitting it and there are so many methods to reduce the image size and hence the bandwidth of the signal. This paper shows wavelet based compression methods. Discrete Wavelet Transform is a powerful tool for analyzing the signals. With the help of a thresholding function, it will be more useful in compression, but this method has some disadvantages like selectivity and shift invariance. So a much better method is proposed. In the proposed method a completely new set of wavelets were designed with lifting filters. Arithmetic coding is combined with proposed algorithm for reducing the size of images with less mean square error (MSE) at high compression ratio (CR).

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A Review on Blind and Non-Blind Deblurring Techniques in Image processing

Vol.1  No.2

Year : 2014

Issue : Apr-Jun

Title : A Review on Blind and Non-Blind Deblurring Techniques in Image processing

Author Name : M.Sahithya, Irala Suneetha, N PUSHPALATHA

Synopsis :

Image blur is a very difficult problem. Now a days, deblurring plays a vital role in digital image processing. Image deblurring has infinite solutions which are unstable because it is used to make the picture sharp and useful. So, to find the best solution for adjusting the regularization parameter, the number of iterations of the algorithm are used to address the optimization problem. This method well estimates the recovered image, but the residual image is a poorly deblurred image and it exhibits structured artifacts that are not spectrally white. Now in the proposed criterion, the same procedure mentioned above is repeated and the regularization parameter is chosen and the algorithm is decided to stop at the best Signal to Noise Ratio (SNR). Tests will be performed on monochrome and color images with various synthetic and real life degradations to get better results.

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DWT Based Pan-sharpening of Low Resolution Multispectral Satellite Images

Vol.1  No.2

Year : 2014

Issue : Apr-Jun

Title : DWT Based Pan-sharpening of Low Resolution Multispectral Satellite Images

Author Name : mamathasashi, M.V.Lakshmaiah , V. Sumalatha , S.Varadarajan

Synopsis :

In this paper, a technique for enhancing the low resolution multi spectral image using Discrete Wavelet Transform (DWT) is proposed with the help of the corresponding high resolution panchromatic image. Image fusion, also called pansharpening, is a technique used to integrate the geometric detail of a high-resolution panchromatic (PAN) image and the color information of a low-resolution Multi spectral (MS) image to produce a high-resolution MS image. Remote Sensing systems, particularly those deployed on satellites, provide a redundant and consistent view of the Earth. In order to meet the requirements of different remote sensing applications, the systems offer a wide range of spectral, spatial radiometric and temporal resolutions. In general, sensors characterized with high spectral resolution, do not have an optimal spatial resolution, which may be inadequate to specific task identification in spite of its good spectral resolution. In panchromatic image with high spatial resolution, detailed geometric features can be easily recognized, while the multispectral images contain richer spectral information.

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Detection of Tumor objects for color based images using k-means

Vol.1  No.2

Year : 2014

Issue : Apr-Jun

Title : Detection of Tumor objects for color based images using k-means

Author Name : geethanjali veerapalli, Reddy PVN

Synopsis :

The brain is a highly dedicated organ. It serves as the control center for body functions. Words, events, thoughts, and feelings are centered in the brain. Each part of the brain has a specific, important function, often a great key function, and each part contributes to the healthy functioning of our body. The position of tumors in the brain shows its effect on individual's functioning and the symptoms caused by tumor. A color based segmentation technique that uses the kmeans clustering method is proposed to track the tumor objects in the Magnetic Resonance (MR) brain images. The key perception in color-based segmentation algorithm with K-means is to convert a given gray-level MR image into a color space image. Then the position of tumor objects is separated from other items of an MR image by using K-means clustering and histogram-clustering. In this paper, the K-means clustering algorithm is used for image segmentation and detecting the tumor objects that are found in the MR brain image.

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Detection Of Changes In Multi-Temporal Images Using Undecimated Discrete Wavelet Transform And Active Contours

Vol.1  No.2

Year : 2014

Issue : Apr-Jun

Title : Detection Of Changes In Multi-Temporal Images Using Undecimated Discrete Wavelet Transform And Active Contours

Author Name : hemanth kuamr, Meenakshi V, milindapurna

Synopsis :

In this paper, an unsupervised change detection method is proposed for satellite images and optical images. The multitemporal data and two discriminate areas of the land covers are changed between different time instances due to its robustness in opposition to noise. The UDWT is exploited to obtain a multi-resolution representation of the difference image which are taken from two satellite images acquired from the same geographical area but at different time instances. For multi-resolution representation of the difference image, a region-based active contour model is applied, then it is segmented into unchanged and changed regions. The proposed change detection method has been conducted based on the optical images. The extensive model results show that, the proposed change detection method constantly gives a better performance.

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Emotional Face Recognition System For Decision Making Based On Statistical Features Of Q-Matrix

Vol.1  No.2

Year : 2014

Issue : Apr-Jun

Title : Emotional Face Recognition System For Decision Making Based On Statistical Features Of Q-Matrix

Author Name : C. NagaRaju, SharadMani , Vijay kumar , Shoba Bindu C

Synopsis :

Human emotions play an important role in decision making. Facial expression is natural and one of the most powerful immediate means for human beings to communicate their emotions and intentions. The face can express emotion sooner than people verbalize their feelings.As technology is advancing, the demand of face emotions recognition is increasing day by day for decision making. In past years, many researchers had introduced different techniques and different algorithms for accurate and reliable face emotion recognition. One key step in facial expression recognition is to extract the low dimensional discriminative features before the feature data are fed into classifier for emotions. In this paper, the authors have presented a new method of facial expression recognition based on statistical parameters of Qmatrix. The problem addressed here is to determine which features optimize classification rate so that, such features may be used in the evaluation of statistical features for face emotion recognition. The Q-matrix is much better than that of the remaining matrix methods, because it considers all possible neighbors of elements at once. The experimental results also demonstrate significant performance improvements due to the consideration of facial movement features and promising performance under face registration errors.

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