An Enhanced Low Rank Approximation SVD-Based Method for Image Denoising

Nonlocal self-similarity of images has attracted considerable interest in the field of image processing and has led to several state-of-the-art image denoising algorithms, such as block matching and 3-D, principal component analysis with local pixel grouping, patch-based locally optimal wiener, and spatially adaptive iterative singular-value thresholding. In this paper, we propose a computationally simple denoising algorithm using the nonlocal self-similarity and the low-rank approximation (LRA). The proposed method consists of three basic steps. First, our method classifies similar image patches by the block-matching technique to form the similar patch groups, which results in the similar patch groups to be low rank. Next, each group of similar patches is factorized by singular value decomposition (SVD) and estimated by taking only a few largest singular values and corresponding singular vectors. Finally, an initial de-noised image is generated by aggregating all processed patches. For low-rank matrices, SVD can provide the optimal energy compaction in the least square sense. The proposed method exploits the optimal energy compaction property of SVD to lead an LRA of similar patch groups. Unlike other SVD based methods, the LRA in SVD domain avoids learning the local basis for representing image patches, which usually is computationally expensive. The experimental results demonstrate that the proposed method can effectively reduce noise and be competitive with the current state-of-the-art denoising algorithms in terms of both quantitative metrics and subjective visual quality.

Evaluation of Data Compression Techniques for Video Transmission over Wireless Sensor Networks

Video transmission over WSN is a challenging task because, video data is inherently huge in size and its transmission requires high bandwidth and processing requires more memory and more power. Presently majority of the research work in this area is focused on improving battery life, optimizing network parameters and increasing energy efficiency. The area of best suitable compression scheme for WSN is rarely being explored. This paper focuses on finding out a suitable compression technique for video transmission over WSN. Wireless sensor network (WSN) is an ad-hoc network of sensor nodes, where each node is able to communicate with every other node in the network in single hop or multi-hop manner. It is a low cost, low power and low bandwidth network with each node having limited battery life and limited memory. In order to overcome these challenges, initially image compression is applied on each frame extracted from given video. To find out most suitable compression technique for WSN, the various existing 2D image transforms like DCT, KLT, Slant, Hartley, Hadamard and DST are compared based upon their energy compaction property. The transform that gives more energy compaction is best suitable for WSN. Thus, after selecting a suitable transform for WSN domain, it is applied to obtain compressed video frames. Zigbee protocol based hardware setup is used for serial transmission of RGB video data frames between the nodes. Different parameters are evaluated for received image frames and transmitted image frames. Experimental evaluation shows that zigbee hardware setup improves the reliability and efficiency of video data transmission. This type of WSN set-up can be used for capturing video in any remote and hard-lying (border, mountains, forest) area, where any other types of networks are not available.

Transform based Speech Enhancement for Auditory Signals based on Iterative Weiner Filtering

One of the important features of Speech processing is speech enhancement. In a noisy environment, speech enhancement plays a vital role. Many research works are being done in speech enhancement methods in recent years but still, it can't be attained. It mainly depends on Speech intelligibility which can improve the speech quality. In this research work, signal representation is considered and the various transforms are applied and compared. The analysis is done with the help of two parameters and the results are compared. Here the enhancement process is focused on using Advanced DCT (ADCT) and Discrete fractional Cosine transform. The ADCT has the advantage of energy compaction and flexible window switching. Iterative Wiener Filtering is used for filtering the coefficients. Pitch Synchronous Analysis (PSA) is combined for finding the exact pitch period.

An Enhancement of Data Hiding Imperceptibility using Slantlet Transform (SLT)

This study proposes a hybrid technique in securing image data that will be applied in telemedicine in future. Based on the web-based ENT diagnosis system using Virtual Hospital Server (VHS), patients are able to submit their physiological signals and multimedia data through the internet. In telemedicine system, image data need more secure to protect data patients in web. Cryptography and steganography are techniques that can be used to secure image data implementation. In this study, steganography method has been applied using hybrid between Discrete Cosine Transform (DCT) and Slantlet Transform (SLT) technique. DCT is calculated on blocks of independent pixels, a coding error causes discontinuity between blocks resulting in annoying blocking artifact. While SLT applies on entire image and offers better energy compaction compare to DCT without any blocking artifact. Furthermore, SLT splits component into numerous frequency bands called sub bands or octave bands. It is known that SLT is a better than DWT based scheme and better time localization. Weakness of DCT is eliminated by SLT that employ an improved version of the usual Discrete Wavelet Transform (DWT). Some comparison of technique is included in this study to show the capability of the hybrid SLT and DCT. Experimental results show that optimum imperceptibility is achieved.