Compressive sensing based scalable video coding for space applications

Image encryption based on compressive sensing and chaos systems

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.

The Discrete Wavelet Transform (DWT) is a signal processing technique popularised by its results in data compression. Considerable work has been done in designing novel architectures to perform the DWT, including a systolic architecture designed by the authors, but little attention has been given to the inverse DWT which is needed in applications such as data compression for signal reconstruction. Despite the fact that the inverse DWT is computationally the reverse of the DWT, the hardware design for the architecture is not simply mirrored. Existing designs expect the architecture for the inverse DWT to be a simple follow-on step from the DWT design, however this is not the case. We present one such problem here, showing the FIFO buffering required on the input of the inverse architecture. We show how the size of this buffer can be calculated and compare it to a fixed data array implementation. This work is based on our systolic array design and is an integral part of the inverse DWT design we are working on for image and video compression.

In this paper, discrete wavelet transform (DWT) is applied on an RGB image and then eigen decomposition is done on original L-L (low-low) components. Similar operation is done on a watermark image. Then two unitary and one diagonal matrices are combined to form a digital watermarked image applying inverse discrete wavelet transform (IDWT). The diagonal component of original image is transmitted through secured channel. At the receiver end, the watermark image is recovered using the watermarked image and diagonal component of the original image. Finally, we compare the original and recovered image and got perfect cross-correlation. We also compare the model of the paper with three previous works in context of processing time and cross-correlation under noisy environment and found better results.

Compressive sensing (CS) is a new technique that give an approach to reconstruct the signal with few numbers of observations or measurements. The CS is based on L1 -norm minimizations to find the sparse solutions and it is known as basis pursuit. In this investigation, CS scheme with different transforms is proposed. The three utilized transform techniques are: Discrete Fourier Transform(DFT), Discrete Cosine Transform(DCT) and Discrete Wavelet Transform(DWT) with daubechies1(DB1) and coiflets1(coif1) basis. The proposed system is tested by employing the following signals: Blocks, Heavy Sine, ‘Bumps’ and ‘Doppler’ which cover wide range of applications. The four testing signals are represented in sparse domain using different transforms. In order to threshold the coefficients of the signals in sparse domain, the universal threshold is utilized in the case of CS with FFT and DWT whereas, the universal threshold is modified to prune the DCT coefficients. The main aim of this study is to investigate the differences among CS with DFT, CS with DCT and CS with DWT, and consequently a suitable transform domain used with CS to be selected. The comparative study is established by assessing the performance of the proposed system using Root Mean Square Error (RMSE), output SNR, and the time required to reconstruct approximated signals. Simulation results have shown that the CS with DWT outperforms the CS with FFT and DCT. CS with DWT has achieved good RMSE values about (0.0014 to 3.359e-8) even when half of the signal elements are removed. CS with FFT and DCT enhanced the noisy Blocks and Bumps signals by 3dB and 1dB respectively, while it is failed to enhance noisy Heavy Sine and Doppler signals. CS with DWT of two basis and for single decomposition level have improved the noisy Blocks, Bumps, Heavy Sine and Doppler signals by 5dB, 4dB, 3dB, and 3dB respectively.

In this paper, we proposed a imitated light field image architecture based on Haar discrete wavelet transform for light field instrument. We can get different characteristic from subband images by using discrete wavelet transform (DWT) decomposition. Each subband contains different energy and edge type. The image is composed of many subbands by using high-order discrete wavelet transform and makes more combination with these subbands. Energy of each subband can be controlled by weight assignment. The blurry image is reconstructed by using inverse discrete wavelet transform (IDWT) with details. We can find a set of coefficients to control image from blur to clear in our simulation. We implement the discrete wavelet transform by adopting cell-based design flow to perform the architecture. The experimental results show the proposed architecture performs imitated light field image architecture based on Harr discrete wavelet transform.

Digital image-processing technique is used in image super-resolution for a variety of applications. In this paper, we propose hybrid domain feature-based image super-resolution using a fusion of Average Pixel Values Technique (APVT) and Discrete Wavelet Transform (DWT). The low-resolution (LR) images are considered and converted into high-resolution (HR) images using a novel technique of APTV by inserting an average of rows between rows and average of columns between columns to get HR images. The DWT is applied on HR images to obtain four bands. The HR images are downsampled, and to enhance image quality, histogram equalization (HE) is utilized. The LL band and HE matrix are added to obtain new LL band. The inverse DWT is applied on four bands to derive SR image. It is observed that the performance of the proposed method is better than existing methods.

Though widely used in image classification, convolutional neural networks (CNNs) are prone to noise interruptions, i.e. the CNN output can be drastically changed by small image noise. To improve the noise robustness, we try to integrate CNNs with wavelet by replacing the common down-sampling (max-pooling, strided-convolution, and average pooling) with discrete wavelet transform (DWT). We firstly propose general DWT and inverse DWT (IDWT) layers applicable to various orthogonal and biorthogonal discrete wavelets like Haar, Daubechies, and Cohen, etc., and then design wavelet integrated CNNs (WaveCNets) by integrating DWT into the commonly used CNNs (VGG, ResNets, and DenseNet). During the down-sampling, WaveCNets apply DWT to decompose the feature maps into the low-frequency and high-frequency components. Containing the main information including the basic object structures, the low-frequency component is transmitted into the following layers to generate robust high-level features. The high-frequency components are dropped to remove most of the data noises. The experimental results show that WaveCNets achieve higher accuracy on ImageNet than various vanilla CNNs. We have also tested the performance of WaveCNets on the noisy version of ImageNet, ImageNet-C and six adversarial attacks, the results suggest that the proposed DWT/IDWT layers could provide better noise-robustness and adversarial robustness. When applying WaveCNets as backbones, the performance of object detectors (i.e., faster R-CNN and RetinaNet) on COCO detection dataset are consistently improved. We believe that suppression of aliasing effect, i.e. separation of low frequency and high frequency information, is the main advantages of our approach. The code of our DWT/IDWT layer and different WaveCNets are available at https://github.com/CVI-SZU/WaveCNet.

To evaluate discrete wavelet transform coefficients and identify descriptors of pattern electroretinogram (PERG) waveforms in order to determine PERG characteristics for optimizing the diagnosis of early primary open-angle glaucoma (POAG). Pattern electroretinogram was performed in 30 normal eyes and 30 eyes with primary open-angle glaucoma according to the ISCEV protocol. The check size was 0.8° and 16°, and the color was black/white in both groups. The results were analyzed in time domain (TD) and discrete wavelet transform (DWT) using the MATLAB software. The mean value, standard deviation, and relative energy of level 6 and 7 detail coefficients (d6, d7) and level 7 approximation coefficients (a7) of Daubechies 4 (db4), Daubechies 8 (db8), Symlet 5 (sym5), Symlet 7 (sym7), and Coiflet 5 (coif5) wavelets were calculated. In all the mentioned wavelets, DWT descriptors were extracted. Signals were reconstructed by inverse DWT. All data obtained by TD and DWT analyses were compared between the two groups. In both check sizes, a significant attenuation of N95 amplitude was seen in the patient group. The relative energy of a7 of db8 increased significantly in the POAG group in the 0.8° check size. In larger check stimuli, the relative energy of d7 of coif5 decreased significantly and the standard deviation of d7 of sym7 increased markedly in glaucomatous patients (P < 0.05). In small stimuli, N95 descriptor (7N) of db8 had the highest value and showed a significant increase as compared to the POAG group. In the 16° check size, there was no significant difference. A strong correlation was seen between reconstructed signals and originals (r = 0.99). The DWT can quantify PERG responses more accurately. In agreement with TD and wavelet coefficients domain results, 7N of db8 decomposition can be used as a good indicator for early detection of POAG.

We propose an algorithm that shows improvement in computation timeof existing image encryption-decryption methods. The algorithm also introduces additional one-level of encryption key into the existing methods. Image compression properties of the DWT (Discrete Wavelet Transform) are utilized to bring the improvement. The new algorithms retain MSE between input and decrypted images and the robustness of existing algorithms.We assume an image to be consisting of three primary color channels viz. R, G and B. An image is at first segregated into these channels and then these proposed methods compress input image channels by using DWT2 (2-D Discrete Wavelet Transform). The compressed image channels are encrypted using 2-D FRT (The 2-D fractional Fourier transform) and random phase masks in two successive steps. The encrypted channels are merged using IDWT2 (2-D Inverse Discrete Wavelet Transform), generating a color encrypted image. Decryption is the inverse of the encryption process.Simulations are performed and the results of these simulations verify the proposals made in the new algorithms.

An Image Resolution Enhancement Technique based on Interpolation of the high frequency sub-band of colour images obtained by Discrete Wavelet Transform and the input colour image is proposed in this paper. Interpolation determines the intermediate values on the basis of observed values. One of the commonly used interpolation technique is Bicubic Interpolation. The edges are enhanced by introducing an intermediate stage by using Stationary Wavelet Transform. It is designed to overcome the lack of Translation-Invariance of Discrete Wavelet Transform. This is widely used in Signal Denoising and Pattern Recognition. Discrete Wavelet Transform is applied in order to decompose an input colour image into different sub-bands. Then the high frequency sub-bands as well as the input colour image are interpolated separately. The interpolated high frequency sub-bands and the Stationary Wavelet Transform high frequency sub-bands have the same size which means they can be added with each other. The new corrected high frequency sub-bands can be interpolated further for higher enlargement. Then all these sub-bands are combined with interpolated input image for new high resolution image by using Inverse Discrete Wavelet Transform. This has been done by MATLAB. The Peak Signal-Noise Ratio was obtained upto 5dB greater than the conventional and state-of-art image resolution enhancement techniques.

In this paper, we present a 2D Logistic-Sine-Cosine map (2D-LSCM) which is based on the classical 2D Logistic, Sine and Cosine maps. Performance analysis shows that it has larger chaotic range, bigger Lyapunov exponent and more complex chaotic behavior than the classical 2D Logistic map. By means of 2D-LSCM, Discrete Wavelet Transform (DWT) and Double Random-Phase Encoding (DRPE), we proposed an effective image encryption algorithm. In this scheme, after conducting DWT on the original image, the wavelet coefficients are scrambled by using the 2D-LSCM, and then the DRPE is employed to encode the scrambled result. Next, the Inverse Discrete Wavelet Transform (IDWT) is conducted on the encoded results. At last, the result from IDWT is diffused and confused with the chaos sequences generated from 2D-LSCM. Simulation results and security analysis reveal the fact that the proposed algorithm has good statistical property, large key space and effective resistance to common cryptanalytic attacks.

The field of image processing is constantly evolving in the last decades and it is being used in many applications. At present there are many enhancement techniques are available for contrast enhancement of the image. This paper proposes an effective approach to improve the quality of the image based on the image fusion using the discrete wavelet transform (DWT). Contrast enhancement approach is proposed for images which has low contrast. Contrast enhancement involves transforming the intensity of pixels from the original value to new value. The goal of contrast enhancement is to improve visibility of image details without introducing unwanted visual appearances and/or unwanted artefacts. This paper presents the contract enhancement technique to visualize all available information in an image. Proposed method uses the retinex algorithm and gamma correction technique. It uses the DWT for decomposition of the image and decompose the image into a set of band-limited components, called HH, HL, LH, and LL. LL subband will be fused to get the better result of the image. Finally, inverse DWT will be performed on fused image to achieve the final enhanced image.

This paper presented a new watermarking method for convert communication and copyright protection. For improving the transparency and robustness of watermarking or information hiding, a visual model was employed to calculate the block based just noticeable difference (JND), and the three detail sub-bands of block discrete wavelet transform (DWT) were selected for embedding objects. Firstly, a watermark sequence binarized from a text information file or image file was encoded by redundant encoding and scrambled chaotically. Then, an image block was selected each time from the carrier image, and transformed by DWT. And then according to the JND and the watermark code, the coefficients at same position of three detail sub-bands were adjusted to hide the information. Lastly, the inverse DWT was executed to get the information hidden image. The experimental results show that the hidden information is invisible, and the algorithm is robust to general image processing operations. The conclusion is that the algorithm is effective and practical.

This paper presents a new robust hybrid multiple watermarking technique using fusion of discrete wavelet transforms (DWT), discrete cosine transforms (DCT), and singular value decomposition (SVD) instead of applying DWT, DCT and SVD individually or combination of DWT-SVD / DCT-SVD. For identity authentication purposes, multiple watermarks are embedded into the same medical image / multimedia objects simultaneously, which provides extra level of security with acceptable performance in terms of robustness and imperceptibility. In the embedding process, the cover image is decomposed into first level discrete wavelet transforms where the A (approximation/lower frequency sub-band) is transformed by DCT and SVD. The watermark image is also transformed by DWT, DCT and SVD. The S vector of watermark information is embedded in the S component of the cover image. The watermarked image is generated by inverse SVD on modified S vector and original U, V vectors followed by inverse DCT and inverse DWT. The watermark is extracted using an extraction algorithm. Furthermore, the text watermark is embedding at the second level of the D (diagonal sub-band) of the cover image. The security of the text watermark considered as EPR (Electronic Patient Record) data is enhanced by using encryption method before embedding into the cover. The results are obtained by varying the gain factor, size of the text watermark, and cover medical images. The method has been extensively tested and analyzed against known attacks and is found to be giving superior performance for robustness, capacity and reduced storage and bandwidth requirements compared to reported techniques suggested by other authors.