Image compression is the application of Data compression on digital images. A fundamental shift in the image compression approach came after the Discrete Wavelet Transform (DWT) became popular. To overcome the inefficiencies in the JPEG standard and serve emerging areas of mobile and Internet communications, the new JPEG2000 standard has been developed based on the principles of DWT. In this re...

The real world signals do not exist without noise. Wavelet Transform based denoising is a powerful method for suppressing noise in signals. In this paper, signal denoising based on Double-Density Discrete Wavelet Transform (DDDWT) and Dual-Tree Discrete Wavelet Transform (DTDWT) methods are implemented with optimum values of threshold point and level of decomposition. Based on the intensity of ...

The wavelet transform is denoted by ‘WT’ gained wide-ranging approval in processing signal and in compressing image. A 2D analysis must be implemented to use WT for processing the image. Such powerful signal analysis technique can be implemented for non-stationary data using Lifting architecture for the Discrete Wavelet Transform (DWT). Since high speed implementation with low latency is a dema...

The main objective of super-resolution (SR) imaging is to reconstruct a high-resolution (HR) image of a scene from one or more low-resolution images of the scene. In resolution enhancement of images, the main loss is on the high frequency components (edges) of the image. This is due to the smoothing caused by interpolation. Hence in order to enhance the quality of the super resolved image, pres...

In deep networks, the lost data details significantly degrade performances of image segmentation. this paper, we propose to apply Discrete Wavelet Transform (DWT) extract during feature map down-sampling, and adopt Inverse DWT (IDWT) with extracted up-sampling recover details. On popular segmentation U-Net, SegNet, DeepLabV3+, design wavelet integrated networks for (WaveSNets). Due effectivenes...

In the computation of forward and inverse Discrete Wavelet Transform (DWT) using Cellular Neural Network (CNN), templates that mimic Haar wavelet are used. It is well known that Daubechies wavelet is the most popular wavelet used in the computation of DWT coefficients. This paper presents an approach towards the design of templates that mimic Daubechies wavelets. A global search algorithm, name...

The Integer Wavelet Transform (IWT), applied to lossy image compression, yields rate-distortion performance which are generally slightly inferior to the Discrete Wavelet Transform (DWT). In this paper, we propose a simple method which, based on the analysis of the IWT signal representation mechanism, is able to raise the IWT performance up to the DWT level. The method can be pro tably employed ...

Abstract. In this paper, a modified version of the discrete wavelet transform (DWT), distinguishing itself with visibly improved space localization properties and noticeably extended potential capabilities, is proposed. The key point of this proposal is the full decorrelation of wavelet coefficients across the lower scales. This proposal can be applied to any DWT of higher orders (Le Gall, Daub...

Wavelet mathematical transformation and heart sound signal processing have recently been attracting a significant amount of attention in the research community. Why is this new priority being given to improved approach to heart sound signal analysis for accurate pattern recognition using the wavelet transform technique? This article provides an overview of this emerging field of digital bio-sig...