Approximation Subband Research Articles

A Gray Scale Image Compression Using Hierarchical DWT Decomposition and Mixing Quantization Techniques

This paper describes a hybrid grayscale compression system based on the discrete wavelet transform (DWT) and a polynomial coding technique for mixing quantization schemes to increase the compression ratio while maintaining quality. The proposed compression system consists of three main steps: first, decomposing an image using a three-level DWT; second, applying the 2-D linear polynomial coding technique to the approximation sub-band; and third, dividing the detailed sub-bands of each level into the Most Significant Value (MSV) and Least Significant Value (LSV), where the former is compressed using iterative scalar uniform quantization and the latter by soft quantization thresholding. For testing the performance of the suggested compression system, five standard images of size 256×256 pixels were adopted. The suggested technique showed superior performance in terms of reconstructed (decoded) image quality and compression ratio (gain), where the compression ratio is between 21–27 with a PSNR value between 36–38 dB and the compression ratio of JPEG is between 7–20 with a PSNR value between 33–37 dB.

Infrared-visible image fusion method based on multi-scale shearing Co-occurrence filter

Infrared-visible image fusion aims to achieve salient targets and abundant texture details. However, most of fusion methods based on multi-scale decomposition fail to effectively preserve detailed and texture information. To avoid these issues, a novel fusion method is proposed by means of multi-scale shearing Co-occurrence filter (MSCOF). This multi-scale decomposition tool possesses the flexible multi-direction and shift-invariant properties of the Shearlet, as well as the smoothing advantages of the Co-occurrence filter (COF). In this study, we obtain approximate and detail subbands by performing MSCOF decomposition on the original images. A weighted fusion rule based on the saliency intensity value (SIV) is performed to combine approximate subbands. Notably, COF is employed on the saliency intensity maps to better preserve edge information of the original image on the fused approximate subbands. Meanwhile, the improved local gradient energy (ILGE) method is utilized to fuse the detail subbands. The results of experimental analysis fully prove that our method is reliable and feasible, and that the obtained objective evaluation metrics are superior to those of some typical methods.

Innovative chaotic dragon fractal (ChDrFr) shapes for efficient encryption applications: a new highly secure image encryption algorithm

In this paper, the generation of new dragon fractal shapes with chaotic iteration parameters is introduced as the main component of a new efficient approach for different cryptographic applications. This process involves applying a chaotic map, which is considered the initiator pattern, to generate different chaotic dragon fractal (ChDrFr) shapes in lieu of lines (which are classically used to generate dragon fractals). This is the new concept of this paper. The used chaotic maps are sensitive to their initial conditions and are characterized by randomness; hence, the resulting scheme is highly secure. As the resulting ChDrFr shapes have sparse structures, the spaces are packed with random values generated from another 5D hyper chaotic map. For encryption applications based on the substitution approach, one of the five generated ChFrDr shapes can be used to construct a chaotic fractal (ChFr) S-Box, while the other four ChDrFr shapes can be used for diffusion purposes. As an application to these new ChDrFr shapes and the ChFr S-Box, we introduce in this paper a new highly secure image encryption algorithm. A Henon chaotic map is used as the initiator of the ChDrFr shapes. The integer wavelet transform (IWT) is used to generate an approximation and three detail sub-bands for the original image. As the approximation sub-band contains a considerable amount of information about the original image, the above-described ChFr S-Box is used as a replacement for each pixel’s value in this sub-band. Then, the resultant substituted image is diffused with one of the generated ChFrDr shapes. The other three ChDrFr shapes are XORed with the details sub-images. Numerical simulation is applied to ensure the efficacy of encrypted images against different attacks. In particular, the correlation coefficient between the initial and the generated images is shown to be nearly zero. Moreover, tests reveal that the information entropy of the encrypted images and UACI were close to their optimum values. The properties of the newly proposed ChDrFr-based encryption algorithm are compared to the ones obtained by other encryption algorithms, and the results prove the superiority of this newly proposed algorithm to other types of encryption methods.

Multilevel histogram shape‐based image watermarking invariant to geometric attacks

AbstractIn this paper, a geometrically invariant image watermarking scheme is proposed by exploiting multilevel histogram shapes. The embedding procedure starts by decomposing the host image with the first level Haar wavelet. After that, histograms are extracted from the approximation subband via several rounds, which are used to embed watermark bits. Each round of embedding first extracts a histogram at a specified level. Then the histogram is split into fragments, into which a number of watermark bits can be embedded. In this way, a considerable watermarking capacity is available. Besides, a histogram adjustment in the first embedding round is suggested to guarantee good population of histogram bins. Experimental results support its robustness against various common attacks and geometric attacks. Moreover, the scheme can embed multiple watermark sequences with various robustness and capacity profiles, which enriches its practical applications.

Early Diagnosis of Neurodegenerative Diseases Using CNN-LSTM and Wavelet Transform.

Early diagnosis of neurodegenerative diseases has always been a major challenge that physicians and medical practitioners face. Therefore, using any method or device that helps with prognostics is of great importance. In recent years, deep neural networks have become popular in medical fields, and the reason is that these networks can help diagnose diseases quickly and precisely. In this research, two novel models based on a CNN-LSTM network are introduced. The main goal is to classify three neurodegenerative diseases, including ALS, Parkinson's disease, and Huntington's disease, from one another and from healthy control patients using the gait signals, which are transformed into spectrogram images. In the first model, the spectrogram images derived from the gait signals are fed into a CNN-LSTM network directly. This model achieved 99.42% accuracy. In the second model, the same input data was used to be classified using a CNN-LSTM network, which uses wavelet transform as a feature extractor before the LSTM unit. During the experiments with the second model, the detail sub-bands were eliminated one by one, and the classification results were compared. Comparing these two models has shown that using the wavelet transform and, in particular, the approximation sub-bands can result in a lighter and faster prognosis with nearly 103 times fewer training parameters overall. The classification result using only approximation sub-bands was 95.37%, using three sub-bands was 94.04%, and including all sub-bands was 94.53%, which is remarkable.

Improved Procedure for Multi-Focus Image Quality Enhancement Using Image Fusion with Rules of Texture Energy Measures in the Hybrid Wavelet Domain

Feature extraction is a collection of the necessary detailed information from the given source, which holds the information for further analysis. The quality of the fused image depends on many parameters, particularly its directional selectivity and shift-invariance. On the other hand, the traditional wavelet-based transforms produce ringing distortions and artifacts due to poor directionality and shift invariance. The Dual-Tree Complex Wavelet Transforms (DTCWT) combined with Stationary Wavelet Transform (SWT) as a hybrid wavelet fusion algorithm overcomes the deficiencies of the traditional wavelet-based fusion algorithm and preserves the directional and shift invariance properties. The purpose of SWT is to decompose the given source image into approximate and detailed sub-bands. Further, approximate sub-bands of the given source are decomposed with DTCWT. In this extraction, low-frequency components are considered to implement Texture Energy Measures (TEM), and high-frequency components are considered to implement the absolute-maximum fusion rule. For the detailed sub-bands, the absolute-maximum fusion rule is implemented. The texture energy rules have significantly classified the image and improved the output image’s accuracy after fusion. Finally, inverse SWT is applied to generate an extended fused image. Experimental results are evaluated to show that the proposed approach outperforms approaches reported earlier. This paper proposes a fusion method based on SWT, DTCWT, and TEM to address the inherent defects of both the Parameter Adaptive-Dual Channel Pulse coupled neural network (PA-DCPCNN) and Multiscale Transform-Convolutional Sparse Representation (MST-CSR).

A Hybrid Image Denoising Method Based on Discrete Wavelet Transformation with Pre-Gaussian Filtering

Background/Objectives: At the time of acquisition and transmission noise is embedded with the images. It introduces new but unwanted information (noise) in images. The elimination of noise to analyze such data is an essential step in preprocessing. The purpose of this study is to propose a novel image denoising approach to recover original images at high noise densities without introducing unwanted artifacts. Methods: A new hybrid method based on approximation subband thresholding with pre-Gaussian filtering is presented in this study. Google Colab as a platform and python as a programming language is used for the implementation of the proposed technique. To evaluate the performance Peak Signal to Noise Ratio (PSNR) is chosen. The standard jpeg images (Cameraman, Lena, Astronaut, Cat) have been taken as an input and random noise with different noise ratios (s =0.05,0.20,0.30,0.50) is applied to get the noisy images for the experiment. In random noise scenarios, the proposed method experimented on different grayscale standard images, and performance is compared with different existing methods. Findings: The standard images with different noise ratios are denoised by the proposed method, and the quality of images is calculated in terms of PSNR. The results obtained from the proposed method on different standard images improve PSNR (PSNR= 25.80dB, s =0.50) at high noise levels significantly. Novelty: Gaussian filter improve the quality of images. However, when wavelet decomposition is blended with filtered image and thresholding is applied on approximation band improved the quality of images. Hence, the proposed method has a wide area of application to improve image quality in the field of character recognition, agriculture, medical science, and remote sensing. Keywords: Gaussian Filter; Discrete Wavelet Thresholding; Image denoising; Image Processing

Securing color images using DNA coding and cosine stockwell transformation in wavelet domain

In this paper, a color image encoding algorithm is developed using cosine stockwell transformation and DNA coding in wavelet domain. Firstly, a color image is decomposed into its primary color components and then each component is wavelet transformed, which results four subbands for each component. The approximation subband of wavelet transformation is normalized and DNA coding is used followed by the proposed double phase encoding while the detail wavelet subbands are cosine stockwell transformed to get intermediate sub-images. The modified approximation and detail subbands are arranged by their indexing and pseudo random permutation is used to generate an encrypted image. At the receiver end, on having the exact knowledge of all the keys in correct order the original image can be recovered from the encrypted image. The bands of DCST, Tent Sine map is used to generate the random phase mask and key for pseudo random permutation is used as encryption and decryption keys. Numerical analysis and robustness of proposed work is evaluated on encrypted image to show the superiority of proposed work.

Noise Robust Illumination Invariant Face Recognition Via Bivariate Wavelet Shrinkage in Logarithm Domain

Abstract Recognizing faces under various lighting conditions is a challenging problem in artificial intelligence and applications. In this paper we describe a new face recognition algorithm which is invariant to illumination. We first convert image files to the logarithm domain and then we implement them using the dual-tree complex wavelet transform (DTCWT) which yields images approximately invariant to changes in illumination change. We classify the images by the collaborative representation-based classifier (CRC). We also perform the following sub-band transformations: (i) we set the approximation sub-band to zero if the noise standard deviation is greater than 5; (ii) we then threshold the two highest frequency wavelet sub-bands using bivariate wavelet shrinkage. (iii) otherwise, we set these two highest frequency wavelet sub-bands to zero. On obtained images we perform the inverse DTCWT which results in illumination invariant face images. The proposed method is strongly robust to Gaussian white noise. Experimental results show that our proposed algorithm outperforms several existing methods on the Extended Yale Face Database B and the CMU-PIE face database.

Electronic portal image enhancement based on nonuniformity correction in wavelet domain.

Electronic portal images are one of the most important tools to verify the ongoing radiotherapy treatment through comparison with a reference image generated during treatment planning. In this procedure, two images are geometrically matched by means of visible bone or other landmarks of interest such as implanted fiducials. However, the intrinsically poor contrast and low spatial resolution of portal images can limit imagequality. In this study, we have provided a multiresolution approach to enhance the quality of portal images acquired from the pelvis treatment fields. The main idea behind this work aims at removing some of the image artifacts that conceal the anatomical information. For this purpose, we have applied the homomorphic filtering on the approximation sub-band of wavelet decomposition to enhance local information. Moreover, in order to sharpen the bone edges, wavelet detail sub-bands were weighted to amplify important image details in the reconstruction of the desired enhanced image. The most appropriate image quality measure was chosen according to the image's characteristics in the spatial domain. By considering the characteristics of portal images as the random and nonperiodic texture, high level of noise, and a nonuniform background, three suitable quality measures of images were assessed: edge content, measure of enhancement, and measure of enhancement byentropy. The higher values of these measures indicate the quality improvement in the processed images through our proposed algorithm. Moreover, the subjective evaluation results indicate that the proposed multiresolution approach significantly enhances the perceived quality of images in comparison with original and the similar approach ( ). Our proposed wavelet-based enhancement algorithm successfully reduced image intensity nonuniformity and enhanced anatomical featured information, which drastically improved the objective metrics values. Subjective evaluation of enhanced image confirmed this qualityimprovement.

MantaRayWmark: An image adaptive multiple embedding strength optimization based watermarking using Manta Ray Foraging and bi-directional ELM

Digital watermarking schemes based on a single value of embedding strength do not take into account the local characteristics of the host signal for watermark embedding. Consequently, striking a balance between imperceptibility and the robustness of the watermarking scheme becomes a challenging task. The use of multiple values of the embedding strength provides a solution to this problem. Multiple embedding strength-based watermarking schemes often deploy meta-heuristic techniques to look for locally optimal values of the embedding strength. However, meta-heuristic techniques being inherently slow, such watermarking schemes cannot be applied in real-time applications. This paper proposes MantaRayWmark—a novel image adaptive watermarking scheme that uses Manta Ray Foraging Optimization (MRFO) to optimize the locally relevant multiple embedding strengths (MES) for balancing the imperceptibility and robustness. However, the time-intensive MRFO-based optimization is carried out only once to train a bi-directional ELM (B-ELM) for a set of images. Each training image is subjected to 4-level DTCWT to produce approximation coefficients. For B-ELM training, the approximation coefficients act as the input features and the corresponding MES values (obtained by way of MRFO using imperceptibility and robustness as the optimization criteria) act as the target outputs. Subsequently, given a new image, the trained B-ELM is used to predict the optimized multiple embedding strengths. For watermark embedding, an image is transformed using 4-level DTCWT and the resulting approximation sub-band matrix is decomposed using SVD to get the principal component (PC) matrix. To avoid the false-positive problem, a binary watermark is inserted into the PC matrix using the MES. To ensure the robustness of the proposed scheme against geometric attacks, we have deployed a novel convolution neural network (ConvNet) based geometric correction procedure. Further, image authentication is achieved by applying SVD-based hashing on the watermarked image. To ensure the security of watermark, the watermark is scrambled using transposition cipher. Experimental results reveal that MantaRayWmark achieves a high degree of watermark recoverability from the signed image in the presence of a number of common geometric and image processing attacks, thus proving its robustness. The results also demonstrate the superiority of the proposed MantaRayWmark when compared to the existing state-of-the-art schemes in terms of robustness and imperceptibility. The proposed scheme being time-efficient may find use its in various multimedia applications.

Remote Sensing Image Retrieval Based on 3D-Local Ternary Pattern (LTP) Features and Non-subsampled Shearlet Transform (NSST) Domain Statistical Features

With the increasing popularity of high-resolution remote sensing images, the remote sensing image retrieval (RSIR) has always been a topic of major issue. A combined, global non-subsampled shearlet transform (NSST)-domain statistical features (NSSTds) and local three dimensional local ternary pattern (3D-LTP) features, is proposed for high-resolution remote sensing images. We model the NSST image coefficients of detail subbands using 2-state laplacian mixture (LM) distribution and its three parameters are estimated using Expectation-Maximization (EM) algorithm. We also calculate the statistical parameters such as subband kurtosis and skewness from detail subbands along with mean and standard deviation calculated from approximation subband, and concatenate all of them with the 2-state LM parameters to describe the global features of the image. The various properties of NSST such as multiscale, localization and flexible directional sensitivity make it a suitable choice to provide an effective approximation of an image. In order to extract the dense local features, a new 3D-LTP is proposed where dimension reduction is performed via selection of ‘uniform’ patterns. The 3D-LTP is calculated from spatial RGB planes of the input image. The proposed inter-channel 3D-LTP not only exploits the local texture information but the color information is captured too. Finally, a fused feature representation (NSSTds-3DLTP) is proposed using new global (NSSTds) and local (3D-LTP) features to enhance the discriminativeness of features. The retrieval performance of proposed NSSTds-3DLTP features are tested on three challenging remote sensing image datasets such as WHU-RS19, Aerial Image Dataset (AID) and PatternNet in terms of mean average precision (MAP), average normalized modified retrieval rank (ANMRR) and precision-recall (P-R) graph. The experimental results are encouraging and the NSSTds-3DLTP features leads to superior retrieval performance compared to many well known existing descriptors such as Gabor RGB, Granulometry, local binary pattern (LBP), Fisher vector (FV), vector of locally aggregated descriptors (VLAD) and median robust extended local binary pattern (MRELBP). For WHU-RS19 dataset, in terms of {MAP,ANMRR}, the NSSTds-3DLTP improves upon Gabor RGB, Granulometry, LBP, FV, VLAD and MRELBP descriptors by {41.93%,20.87%}, {92.30%,32.68%}, {86.14%,31.97%}, {18.18%,15.22%}, {8.96%,19.60%} and {15.60%,13.26%}, respectively. For AID, in terms of {MAP,ANMRR}, the NSSTds-3DLTP improves upon Gabor RGB, Granulometry, LBP, FV, VLAD and MRELBP descriptors by {152.60%,22.06%}, {226.65%,25.08%}, {185.03%,23.33%}, {80.06%,12.16%}, {50.58%,10.49%} and {62.34%,3.24%}, respectively. For PatternNet, the NSSTds-3DLTP respectively improves upon Gabor RGB, Granulometry, LBP, FV, VLAD and MRELBP descriptors by {32.79%, 10.34%}, {141.30%, 24.72%}, {17.47%,10.34%}, {83.20%,19.07%}, {21.56%,3.60%}, and {19.30%,0.48%} in terms of {MAP,ANMRR}. The moderate dimensionality of simple NSSTds-3DLTP allows the system to run in real-time.

Toward Authentication of Videos: Integer Transform Based Motion Vector Watermarking

Now a day, digital content like videos, audios and images are widely used as evidence in criminal courts and forensic labs. Due to advanced low-cost and easily available multimedia/ communication tools and softwares, manipulation of the content is a no-brain task. Thus, the protection of digital content originality is a challenge for the content owners and researchers before it can be produced in court or used for some other purpose. In this paper, the motion vector watermarking technique has been proposed that validate/authenticate videos. We are embedding the correlated watermark in the integer wavelet transform domain and the selection of embedding areas is based on the variation of motion vectors. The video frames are fully protected in both spatial and transform domains since the watermark is correlated with the approximation subbands of wavelet transform before embedding. The proposed technique can concisely determine the attacked regions. The results validate the performance of the proposed approach in terms of quality metrics like peak signal to noise ratio (PSNR), structural similarity index (SSIM), normalized coefficients (NC) and bit error rate (BER).

Multimodal medical image fusion using gradient domain guided filter random walk and side window filtering in framelet domain

Since multimodal imaging technology is able to provide multiple perspectives on the lesion, it has become increasingly important in clinical diagnosis and treatment planning. In this paper, a novel medical image fusion using gradient domain-guided filter random walk (GDGFRW) and side window filtering (SWF) in the framelet transform (FT) domain is presented. Firstly, FT is performed on the original multimodal source images to obtain the corresponding approximate and residual representations. Secondly, a novel model—GDGFRW, which combines the superiorities of both gradient domain guided filtering and random walks—is constructed to interpret the approximate sub-bands, while the residual sub-bands are fused by SWF. Finally, the fused approximate sub-bands and residual sub-bands undergo inverse FT to generate the final fused image. To verify its effectiveness, the proposed method was tested on different categories of multimodal medical image fusion issues, in more than 40 pairs of source images. The proposed method outperforms the current representative ones in terms of both subjective visual performance and objective assessment.

Wavelet Frequency Separation Attention Network for Chest X-ray Image Super-Resolution.

Medical imaging is widely used in medical diagnosis. The low-resolution image caused by high hardware cost and poor imaging technology leads to the loss of relevant features and even fine texture. Obtaining high-quality medical images plays an important role in disease diagnosis. A surge of deep learning approaches has recently demonstrated high-quality reconstruction for medical image super-resolution. In this work, we propose a light-weight wavelet frequency separation attention network for medical image super-resolution (WFSAN). WFSAN is designed with separated-path for wavelet sub-bands to predict the wavelet coefficients, considering that image data characteristics are different in the wavelet domain and spatial domain. In addition, different activation functions are selected to fit the coefficients. Inputs comprise approximate sub-bands and detail sub-bands of low-resolution wavelet coefficients. In the separated-path network, detail sub-bands, which have more sparsity, are trained to enhance high frequency information. An attention extension ghost block is designed to generate the features more efficiently. All results obtained from fusing layers are contracted to reconstruct the approximate and detail wavelet coefficients of the high-resolution image. In the end, the super-resolution results are generated by inverse wavelet transform. Experimental results show that WFSAN has competitive performance against state-of-the-art lightweight medical imaging methods in terms of quality and quantitative metrics.

No-Reference Retinal Image Sharpness Metric Using Daubechies Wavelet Transform

Retinal fundus images are increasingly used by ophthalmologists both manually and without human intervention for detecting ocular diseases. Poor quality retinal images could lead to misdiagnosis or delayed treatment. Hence, a picture quality index was a crucial measure to ensure that the obtained images from acquisition system were suitable for reliable medical diagnosis. In this paper, a no-reference retinal image quality assessment based on wavelet transform is presented. A multiresolution Daubechies (db2) wavelet at level 4 was employed to decompose an original image into detail, and approximation sub-bands for extracting the sharpness information. The sharpness quality index was calculated from the entropy of the sub-bands. The proposed measure was validated by using images from the High-Resolution Fundus (HRF) dataset. The experimental results show that the proposed index performed more consistent with human visual perception and outperformed the Abdel-Hamid et al method.

TWEEC: Computer‐aided glaucoma diagnosis from retinal images using deep learning techniques

AbstractA novel two‐branched deep convolutional (TWEEC) network is developed for computer‐aided glaucoma diagnosis. The TWEEC network is designed to simultaneously extract anatomical information related to the optic disc and surrounding blood vessels which are the retinal structures most affected by glaucoma progression. The spatial retinal images and wavelet approximation subbands are compared as inputs to the proposed network. TWEEC's performance is compared to three implemented convolutional networks, one of which employs transfer learning. Experiments showed that the introduced TWEEC network achieved accuracies of 98.78% and 96.34% for the spatial and wavelet inputs, respectively, by that outperforming the other three deep networks by 8‐15%. This work paves the way for the development of efficient deep learning based computer‐aided glaucoma diagnosis tools. Moreover, the present study illustrates that considering specific wavelet subbands for the training of convolutional networks can result in reliable performance with the advantage of reduced overall network training time.

Semi-fragile watermarking scheme based on perceptual hash function (PHF) for image tampering detection

The increasing performances of personal computers, as well as software of image processing, enable the easy manipulation of digital media content. Unfortunately, this easy manipulation makes the detection of changes in the multimedia content a very difficult task. Digital watermarking is among the most appropriate techniques to verify the integrity of multimedia content. In this paper, we propose a semi-fragile watermarking scheme for JPEG2000 image self-authentication that ensuring the robustness of the watermark against the compression attacks generated by the JPEG2000 encoder itself. The scheme is combined with the JPEG2000 encoder by embedding the generated watermark into the host image during the JPEG2000 compression process. To generate the watermark we suppose to use a perceptual hash function (PHF) operating on discrete wavelet coefficients of the host image. The proposed watermark generation process leads the system to verify the integrity of the image without the need to any file except for the watermarked image. The watermark is embedded during the JPEG2000 compression process after Discrete Wavelet Transform (DWT) step into the approximation sub-band coefficients of the five wavelet decomposition using Index Modulation Quantification (QIM), and can be extracted during image decoding. To prove the authentication of the image, the system compares the extracted watermark with the new watermark generated from the received image. Experimental results show that our proposed approach has not only an extremely high accuracy of tampering detection but also a relatively very high resistance against JPEG2000 compression attacks.

A COMPARATIVE STUDY OF VARIOUS WAVELET APPROACHES USED IN IMAGE DENOISING

The world is constantly changing, and vision helps the humans to understand the environmental changes over time. The changes can be seen by, capturing the images. Hence digital image plays a vital role in day to day life. During the process of acquisition of digital image, the qualities of digital pictures are degraded due to additive noise known as adaptive white Gaussian noise. Therefore, the major challenge of image denoising algorithm is to improve the visual appearance while preserving the other details of the image. For the last two decades, wavelet has become an elegant tool in image denoising techniques. Among all wavelet based denoising methods, wavelet thresholding became popular because, wavelet appropriately separates the noisy signal from the image. The wavelet separation leaves the coarse grain noise in approximation sub-band and fine grain noise in detail sub-bands. Therefore, in wavelet based thresholding methods noise in detail sub-bands are threshold and approximate sub-band noise are kept as such. Hence, the efficiency of all wavelet based shrinkage techniques depends on, the choice of threshold parameter, thresholding technique and how the noise in the approximation sub-bands are handled. This paper presents a brief comparative study of denoising techniques proposed in the research articles based on the above parameters for Gaussian noise reduction using various wavelets transform. With the help of these experiments, we are able to identify the strengths and weaknesses of these methods, as well as seek the way ahead towards a definitive solution to the long-standing problem of image denoising.

Artificial bee colony based perceptually tuned blind color image watermarking in hybrid LWT-DCT domain

A perceptually tuned blind digital watermarking method for color images in a hybrid lifting wavelet transform (LWT) and discrete cosine transform (DCT) domain is proposed in this work. The color watermark is scrambled by arnold transform before embedding to enhance the security level. The color host image is firstly transformed by lifting wavelet transform and the approximate sub-band is partitioned into different blocks each of size 8 x 8. The pixel values of each block are quantized using a quantization table. Each quantized-block is further decomposed by the DCT, to generate the AC coefficient matrix. Scrambled watermark is thereafter inserted into the AC coefficient matrix using the perceptually tuned dynamic embedding strength factor. The visual quality and robustness of the proposed watermarking are balanced by optimizing the value of perceptually tuned strength factor by the Artificial bee colony (ABC) optimization algorithm. Experimental results show that the color watermark is undetectable (with CPSNR > 40 dB) into the host image and robust enough (with NC > 0.9) to resist the image processing and manipulation attacks. Comparison analysis with other related watermarking methods highlights the efficacy of the proposed work.