Robust neural network threshold determination for wavelet shrinkage in images

The discrete wavelet transform (DWT) has been established as an effective tool in denoising images. Various studies have developed statistical models for denoising signals in the wavelet domain. In these techniques, the amount of noise is estimated from the detail coefficients of the transform. However, in images rich in textures, this estimate does not accurately reflect the noise levels of the image. In this paper, we introduce a robust method of noise and signal estimation using directional characteristics of an image. A feed-forward neural network is utilized to establish the relationship between the new estimators and the optimal soft threshold. Testing results show equivalent performance to traditional thresholding algorithms in most images. In highly detailed images, the proposed network shows significant improvement in denoising. © 2011 IEEE

Image fusion of multidirectional wavelet transforms for image denoising

Image denoising in the wavelet domain has been attractive to researchers in the past decade due to its suitable properties which lead to a smooth denoised image. However, limitations in edge representation have been found particularly with diagonal edges which may be destroyed in the denoising process. Direction-sensitive variants of the transform have been proposed by various researchers but they are often computationally expensive and difficult to implement in an actual system. This work presents a direction-sensitive technique of denoising based on a reversible rotation process combined with a simple discrete wavelet transform and image fusion methods. Test results on the said methods show significant gains over the traditional wavelet denoising process. Additionally, the simple and non-adaptive nature of the process makes it attractive for software and hardware implementation

Image compression using adaptive discrete wavelet transforms on image seams

The discrete wavelet transform (DWT) is a flexible tool in signal processing. Its use for image processing and particularly the matter of lossless and lossy compression have been recognized in various studies. However, the ability of DWT to effectively represent image data is limited to smooth image regions. Discontinuities in the form of edges are expensive to code. We investigate the use of an adaptive transform to reduce the occurrences of large wavelet coefficients. A direction selection algorithm is introduced to subdivide the image into discrete blocks with each block assigned to an arbitrary direction. Transforms occurring between blocks are adapted to the direction of the concerned pixels to prevent boundary distortions. To encode the coefficients to a bitstream, a data clustering variant of SPIHT is also introduced with the intention of lowering quantization errors for low bitrates. Preliminary test results based on PSNR and SSIM comparisons show a comparable performance to JPEG2000 even without the use of an entropy encoder

Fuzzy-genetic photoplethysmograph peak detection

© 2014 IEEE. Photoplethysmography (PPG) promises noninvasive body metrics measurement, especially that of heart rate. However, this system is prone to noise due to motion artifacts. This paper presents a fuzzy inference system, with membership functions and rules tuned by a genetic algorithm that utilizes the principal components of the PPG data accelerometer data from the x, y, and z coordinates in order to recover the peaks from the distorted PPG signal. A comparative test demonstrated that a 56.66% peak-to-peak correspondence to a reference ECG signal is achievable with the fuzzy-genetic system in place

Image compression using adaptive discrete wavelet transforms on image seams

The discrete wavelet transform (DWT) is a flexible tool in signal processing. Its use for image processing and particularly the matter of lossless and lossy compression hive been recognized in various studies. However, the ability of DWT to effectively represent image data is limited to smooth image regions. Discontinuities in the form of edges are expensive to code. We investigate the use of an adaptive transform to reduce the occurrences of large wavelet coefficients. A direction selection algorithm is introduced to subdivide the image into discrete blocks with each block assigned to an arbitrary direction. Transforms occurring between blocks are adapted to the direction of the concerned pixels to prevent boundary distortions. To encode the coefficients to a bitstream, a data clustering variant of SPIT is also introduced with the intention of lowering quantization errors for low bitrates. Preliminary test results based on PSNR and SSIM comparisons show a comparable performance to JPEG2000 even without the use of an entropy encoder