FDCT-Based Digital Watermarking Algorithm for Gray Images

Image denoising always is one of important research topics in the image processing field. In this paper, fast discrete curvelet transform (FDCT) and undecimated wavelet transform (UDWT) are proposed for image denoising. A noisy image is first denoised by FDCT and UDWT separately. The whole image space is then divided into edge region and non-edge regions. After that, wavelet transform is performed on the images denoised by FDCT and UDWT respectively. Finally, the resultant image is fused through using both of edge region wavelet cofficients of the image denoised by FDCT and non-edge region wavelet cofficients of the image denoised by UDWT. The proposed method is validated through numerical experiments conducted on standard test images. The experimental results show that the proposed algorithm outperforms wavelet-based and curvelet-based image denoising methods and preserve linear features well.

To improve the quality of the fused image, we propose a remote sensing image fusion method based on sparse representation. In the method, first, the source images are divided into patches and each patch is represented with sparse coefficients using an overcomplete dictionary. Second, the larger value of sparse coefficients of panchromatic (Pan) image is set to 0. Third, Then the coefficients of panchromatic (Pan) and multispectral (MS) image are combined with the linear weighted averaging fusion rule. Finally, the fused image is reconstructed from the combined sparse coefficients and the dictionary. The proposed method is compared with intensity-hue-saturation (IHS), Brovey transform (Brovey), discrete wavelet transform (DWT), principal component analysis (PCA) and fast discrete curvelet transform (FDCT) methods on several pairs of multifocus images. The experimental results demonstrate that the proposed approach performs better in both subjective and objective qualities.

To improve the quality of the fused image, we propose a remote sensing image fusion method based on sparse representation. In the method, first, the source images are divided into patches and each patch is represented with sparse coefficients using an overcomplete dictionary. Second, the larger value of sparse coefficients of panchromatic (Pan) image is set to 0. Third, Then the coefficients of panchromatic (Pan) and multispectral (MS) image are combined with the linear weighted averaging fusion rule. Finally, the fused image is reconstructed from the combined sparse coefficients and the dictionary. The proposed method is compared with intensity-hue-saturation (IHS), Brovey transform (Brovey), discrete wavelet transform (DWT), principal component analysis (PCA) and fast discrete curvelet transform (FDCT) methods on several pairs of multifocus images. The experimental results demonstrate that the proposed approach performs better in both subjective and objective qualities.

Noise removal is essential in medical imaging applications in order to enhance and recover anatomical details that may be hidden in the data. The issues of Poisson noise occurrence in medical imaging due to the arrival of photon nature of light produced at the time of capturing of image have always been a concern. This paper addresses a novel approach for the removal of Poisson noise embedded in the biomedical images based on multi-resolution transform-based denoising and fusion approach. In this technique, first, the images are denoised separately, by applying Discrete Wavelet Transform (DWT)/Stationary Wavelet Transform (SWT) and Fast Discrete Curvelet Transform (FDCT) integrated with Rudin-Osher-Fatemi (ROF) model. Next, the distinct features extracted from the denoised images are fused, resulting in an enhanced and denoised image. The proposed method was tested and compared with other denoising techniques on a set of biomedical images. Subjective and objective evaluations show better performance of this new approach compared to existing techniques.

The most vivacious area of the image processing is image enhancement. Newer techniques and ideas are being routinely incorporated to this field for the proper enhancement to make the original image suitable for some specific purposes. However, the techniques that are suitable for X-ray image enhancement may not necessarily be suitable for enhancement of the Satellite Telescope Transmitted (STT) images. Many times, Medical images especially X-ray images are required to be sharper than its original version for proper information extraction that is necessary for diagnostic purposes. Homomorphic filters that actually belong to the group of the sharpening filters can be employed for finding fine details in the X-ray images. The Homomorphic filters use the Discrete Fourier Transforms (DFT) as the core transform. Presently, more efficient tools for transformations are being used; such as the Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), Fast Fourier Transform (FFT) and Fast Wavelet Transform (FWT) in digital image processing. In this paper, along with the conventional DFT technique, DCT, DWT, FFT and FWT are incorporated in the image transformation process that consequently modifies the core process of Homomorphic filters. The performance of the resulting modified Homomorphic filters are compared with that of the original filter. The performance enrichment has become evident from the numerical and the visual comparisons.

Development of fast Discrete Wavelet Transformation (DWT) is desired in many real life applications. It provides time-frequency representation which gives a multi-resolution outlook of the signal. It is also used for de-noising the signal. However, the DWT is computationally intensive and requires high speed computing platform. So, it is desirable to implement DWT on FPGAs as they have a capability of offering pipelined and parallel processing architecture to achieve real time performance. In this work, an efficient implementation of the DWT method on FPGA platform is proposed for all its dimensions (1-D, 2-D and 3-D). Experimental results demonstrate that our optimized implementation can significantly outperform an equivalent implementation. Also, it shows superior performance when compared with other implementations on different platforms.

Image fusion based on the Fourier and wavelet transform methods retain rich multispectral details but less spatial details from source images. Wavelets perform well only at linear features but not at non linear discontinuities because they do not use the geometric properties of structures. Curvelet transforms overcome such difficulties in feature representation. In this paper, we define a novel fusion rule via high pass modulation using Local Magnitude Ratio (LMR) in Fast Discrete Curvelet Transforms (FDCT) domain. For experimental study of this method Indian Remote Sensing (IRS) Resourcesat-1 LISS IV satellite sensor image of spatial resolution of 5.8m is used as low resolution (LR) multispectral image and Cartosat-1 Panchromatic (Pan) of spatial resolution 2.5m is used as high resolution (HR) Pan image. This fusion rule generates HR multispectral image at 2.5m spatial resolution. This method is quantitatively compared with Wavelet, Principal component analysis (PCA), High pass filtering(HPF), Modified Intensity-Hue-Saturation (M.IHS) and Grams-Schmidth fusion methods. Proposed method spatially outperform the other methods and retains rich multispectral details.

The feature extraction plays a very important role in iris recognition. Recent researches on multiscale analysis provide good opportunity to extract more accurate information for iris recognition. In this work, a new directional iris texture features based on 2-D Fast Discrete Curvelet Transform (FDCT) is proposed. The proposed approach divides the normalized iris image into six sub-images and the curvelet transform is applied independently on each sub-image. The anisotropic feature vector for each sub-image is derived using the directional energies of the curvelet coefficients. These six feature vectors are combined to create the resultant feature vector. During recognition, the nearest neighbor classifier based on Euclidean distance has been used for authentication. The effectiveness of the proposed approach has been tested on two different databases namely UBIRIS and MMU1. Experimental results show the superiority of the proposed approach.

Video steganography mainly deals with secret data transmission in a carrier video file without being visually noticeable by intruders. Video steganography is preferred over image steganography because a video carries more space in comparison to an image. The main concept of information hiding consists of a cover media, which is a greyscale or a color video, a secret data, which is an image or text, and a stego key. Here a secure video steganography method has been proposed which uses Curvelet Transform for secret data embedding, Elliptic Curve Cryptography for stego key encryption and a threshold algorithm for the determination of the amount of secret data to be encoded per frame. A video is a collection of various frames. The frames are selected randomly from the cover video and the frame number of the respective frames has been indexed in the stego key to find the secret data embedding location. Here, the selection of frames in a sequential manner has been avoided to improve security. For enhanced security, the stego key is also encrypted using Elliptic Curve Integrated Encryption Scheme (ECIES). Fast Discrete Curvelet Transform (FDCT) has been applied to the frames of the cover video and the curvelet coefficients have been modified to obscure the secret data to produce the stego video.

Ground-roll is an interferential wave existing widely in seismic data, and it is an important step in seismic data processing to suppress ground-roll. Wavelet Transform lacks of detecting the singularities of the signal with straight line or curve, and the method can separate the different waves at the same time because of Curvelet transform simultaneously has the characteristics of frequency and direction. The experiment results show that the Curvelet transform has better effects in ground-roll suppression.

The goal of the present research is to despeckle SAR images, which is critical for segmentation and target recognition in satellite SAR images. When a despeckling algorithm is applied to a SAR image, important information such as the edges, corners, textures, and object parts will degrade. Curvelet transform is a recently proposed form of multi-scale analysis that achieves better performance of wavelet and Gabor transforms in edge and curve detection. This is a geometric transform that is useful for SAR image processing. For unsupervised texture images, segmentation is different and distinct from the textures, so the textures at the boundary noises will disappear. Curvelet transform has produced good results in the detection of curved edges with higher accuracy in finding the orientation than wavelet transforms. The present study uses fast discrete curvelet transform (FDCT) based on wresting and uses unsupervised adaptive threshold learning to develop a new despeckling algorithm for SAR images. In the proposed algorithm, each segment of the SAR image can be learned for selection of its adaptive threshold. Simulation results demonstrate that the proposed algorithm performs better than similar methods.

This paper contrasts the implementation of several chaos-based digital image watermarking techniques in terms of imperceptibility, robustness, security and complexity. Most spatial and transform domains methods have been used namely Least Significant Bit (LSB), Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT) and Redundant Discrete Wavelet Transform (RDWT) with Singular Value Decomposition (SVD) mathematical tool which has enhanced embedding capability. Both continuous and discrete chaotic maps namely Arnold's Chaotic Map (ACM), Logistic Chaotic map (LCM) and Lorenz Chaotic Map (LoCM) are included to result in secure schemes. Exhaustive series of tests have been performed along with a range of evaluation metrics for imperceptibility evaluation. In addition, Robustness of all the techniques has also been confirmed by applying a variety of attacks on the resulting images. This study will help to adopt the most suitable methods for a given application as sufficient number of test images have been utilized.

This paper presents a new approach to the design of integer wavelet filters and an efficient VLSI architecture for low power implementation. Instead of using the lifting scheme, power-of-two wavelet coefficients are derived directly from the roots of the half-band filter. Based on the integer wavelets, we introduce a fast integer Discrete Wavelet Transformation (IntDWT). The proposed integer transform for (9,7) filters requires only 17 integer additions and 15 shift operations. It, however, achieves the same coding efficiency as the floating-point approach. In the proposed VLSI architecture, several optimization techniques are used to remove the redundancy of computational units along the data path. The hardware and software simulation results show that both high performance and low power consumption for DWT chipset can be accomplished at the algorithm architecture level.

Image compression emanating utilizations of data compression on digital images. The Discrete Wavelet Transform (DWT) is getting familiar with an extreme move in image processing. To eliminate the drawback in the JPEG standard and rising areas of mobile and Internet communications, the new JPEG2000 standard has been created based on the principles of DWT. With the prevalence of the 2_D DWT, the technology has seen predictable improvement. All things considered, there is still a need for more robust and efficient compression technology using DWT. Hence, a 2_D DWT is implementing with a multiplier-less NEDA technique. NEDA is consisting of a buffer, ROM, and adder. There are many types of adders used in digit circuits but Kogge Stone Adder (KSA) is used in the proposed method. KSA is a very efficient adder and consists of the XOR gate and HA. Proposed Schemes are simulated Xilinx software with the VHSIC Hardware Description Language (VHDL) platform and calculates parameter i.e. speed and frequency.Keywords2_D discrete wavelet transform (DWT)New efficient distributive arithmetic (NEDA)Read only memory (ROM)Half adder (HA)Kogge stone adder (KSA)

Purpose:To design and implement a GPU‐based discrete wavelet transformation (DWT) to be used in medical image reconstruction, image processing, and data compression. DWTs are widely used in medical physics, but the computation of DWTs is time consuming for large volumetric data. An efficient parallel implementation of DWTs is essential for many time sensitive applications, such as 4DCT.Methods:We choose Daubechies wavelet transformations as a benchmark, implementing both DWT and inverse DWT (IDWT). The reference CPU code is from “Numerical Recipes in C”. We implemented GPU‐based codes using C++ with CUDA 7.5. A GPU is specialized processors with a highly parallel structure originally designed for manipulating computer graphics. GPU computation is highly memory‐bounded. To optimize GPU memory access pattern and achieve best performance for transformation along Y‐direction, the data are transposed before and after the DWT (IDWT), which can sharply reduce computing efforts. Both CPU and GPU codes are unit‐tested and the result differences between two implementations are verified to be within rounding error. The hardware platform was a desktop with Intel Xeon E5‐1607 3.00 GHz CPU and NVIDIA Quadro K2000 GPU.Results:With the GPU implemented code, we process a medical CT image size of 512×512×256 in 0.5 seconds, which is 60 times faster than the CPU implementation. A naive non‐optimized GPU implementation by direct parallelization approaches only 10 times speedup for 2‐D and 3‐D DWT and IDWT. In comparison, the optimization of GPU memory access pattern can obtain an extra 6x speedup.Conclusion:We developed an efficient implementation of GPU‐based DWT/IDWT for medical image processing. To achieve the best performance of any medical imaging processing algorithm, developers should pay attention to memory access pattern optimization when implementing on GPU architecture.This project is supported by CPRIT grant under RP150485.