Correlation Of Images Research Articles

An Adaptive Region-Based Transformer for Nonrigid Medical Image Registration With a Self-Constructing Latent Graph.

Nonrigid registration of medical images is formulated usually as an optimization problem with the aim of seeking out the deformation field between a referential-moving image pair. During the past several years, advances have been achieved in the convolutional neural network (CNN)-based registration of images, whose performance was superior to most conventional methods. More lately, the long-range spatial correlations in images have been learned by incorporating an attention-based model into the transformer network. However, medical images often contain plural regions with structures that vary in size. The majority of the CNN- and transformer-based approaches adopt embedding of patches that are identical in size, disallowing representation of the inter-regional structural disparities within an image. Besides, it probably leads to the structural and semantical inconsistencies of objects as well. To address this issue, we put forward an innovative module called region-based structural relevance embedding (RSRE), which allows adaptive embedding of an image into unequally-sized structural regions based on the similarity of self-constructing latent graph instead of utilizing patches that are identical in size. Additionally, a transformer is integrated with the proposed module to serve as an adaptive region-based transformer (ART) for registering medical images nonrigidly. As demonstrated by the experimental outcomes, our ART is superior to the advanced nonrigid registration approaches in performance, whose Dice score is 0.734 on the LPBA40 dataset with 0.318% foldings for deformation field, and is 0.873 on the ADNI dataset with 0.331% foldings.

A modified deep learning method for Alzheimer’s disease detection based on the facial submicroscopic features in mice

Alzheimer’s disease (AD) is a chronic disease among people aged 65 and older. As the aging population continues to grow at a rapid pace, AD has emerged as a pressing public health issue globally. Early detection of the disease is important, because increasing evidence has illustrated that early diagnosis holds the key to effective treatment of AD. In this work, we developed and refined a multi-layer cyclic Residual convolutional neural network model, specifically tailored to identify AD-related submicroscopic characteristics in the facial images of mice. Our experiments involved classifying the mice into two distinct groups: a normal control group and an AD group. Compared with the other deep learning models, the proposed model achieved a better detection performance in the dataset of the mouse experiment. The accuracy, sensitivity, specificity and precision for AD identification with our proposed model were as high as 99.78%, 100%, 99.65% and 99.44%, respectively. Moreover, the heat maps of AD correlation in the facial images of the mice were acquired with the class activation mapping algorithm. It was proven that the facial images contained AD-related submicroscopic features. Consequently, through our mouse experiments, we validated the feasibility and accuracy of utilizing a facial image-based deep learning model for AD identification. Therefore, the present study suggests the potential of using facial images for AD detection in humans through deep learning-based methods.

Quantum Color Image Encryption: Dual Scrambling Scheme based on DNA Codec and Quantum Arnold Transform

Abstract In the field of Internet, image is of great significance to information transmission. Meanwhile, how to ensure and improve its security has become the focus of research in the world today. We combine DNA codec with Quantum Arnold Transform (QArT) to propose a new double encryption algorithm for quantum color images. to improve the security and robustness of image encryption.Firstly, we utilize the biological characteristics of DNA codecs to perform encoding and decoding operations on pixel color information in quantum color images, and achieve pixel-level diffusion. Secondly, we use QArT to scramble the position information of quantum images and use the operated image as the key matrix for quantum XOR operations. All quantum operations in this paper are reversible, so the decryption operation of the ciphertext image can be realized by the reverse operation of the encryption process. We conduct simulation experiments on encryption and decryption using three color images of ”Monkey”, ”Flower” and ”House”. The experimental results show that the peak value and correlation of the encrypted images on the histogram have good similarity, and the average NPCR of RGB three-channel is 99.61%, the average UACI is 33.41%, and the average information entropy is about 7.9992. In addition, the robustness of the proposed algorithm is verified by the simulation of noise interference in the actual scenario.

A Lightweight Image Encryption based on Improving LED using Rossler Attractor

Lightweight image encryption refers to the use of encryption algorithms designed to secure images while considering the limitations of resources, such as processing power and memory, typically found in devices like IoT devices, mobile phones, and embedded systems. This paper proposes a lightweight image encryption based on an LED encryption algorithm and a method for a pseudo number generator called Rossler attractor. Image is split into their three-color bands (RGB) and then partitioned into a block for encryption, a three-equations model (Rossler Attractor) is used for a pseudo number generator, a specific operation applied for producing key scheduled for rounds in LED to seem as random as possible. The experiment explains the objective test to ensure the performance. The contribution of this work is to present an efficient method for lightweight encryption that is applied to color images with a pseudo number generator-based Rossler attractor dependent on a three-dimensional equation. The technique makes it sensitive to initial conditions and could seem like dynamic encryption that resisted many types of attacks. Visually test represented by the histogram of the image before and after encryption is noted that the result is uniform and differs from the input one. The correlation of the output image is a low value which means no correlation is there between pixels horizontal, vertical and diagonal. The similarity test also applied such as MSE, PSNR and SSIM which also explain no similarity between plain and encrypted images. The randomness of generated numbers was tested using the standard test NIST for evaluation of the input key

A Transformer-based invertible neural network for robust image watermarking

For the existing encoder-noise-decoder (END) based watermarking models, since the coupling between the encoder and the decoder is weak, the encoder generally embeds certain redundant features into the cover image to enable the decoder to extract watermark completely, which will affect watermarking invisibility. To address this problem, this paper proposes a Transformer-based invertible neural network (INN) for robust image watermarking (IWFormer). In order to effectively reduce redundant features, the INN framework is utilized for the watermark embedding and extracting processes, so that the encoded features are highly consistent with the features required for decoding. For enhancing watermarking robustness, an affine Transformer module is designed by mining the global correlation of the cover image. In addition, considering that the human visual system is sensitive to low-frequency variations, the wavelet low-frequency sub-band loss is deployed to guide watermark to be embedded in middle- and high-frequency components, thus further increasing the quality of the encoded images. Experimental results demonstrate that compared with the existing state-of-the-art watermarking models, the proposed IWFormer owns remarkable advantages in terms of both watermarking invisibility and robustness.

Hiding image with inception transformer

AbstractImage steganography aims to hide secret data in the cover media for covert communication. Though many deep‐learning‐based image steganography methods have been presented, these approaches suffer from the inefficiency of building long‐distance connections between the cover and secret images, leading to noticeable modification traces and poor steganalysis resistance. To improve the visual imperceptibility of generated stego images, it is essential to establish a global correlation between the cover and secret images. In this way, the secret image can be dispersed throughout the cover image globally. To bridge this gap, a novel image steganography framework called HiiT is proposed, which takes advantage of CNN and Transformer to learn both the local and global pixel correlation in image hiding. Specifically, a new Transformer structure called Inception Transformer is proposed, which incorporates the Inception Net in the attention‐based Transformer architecture. The Inception Net can learn different scaled image features using multiple convolution kernels, while the attention mechanism can learn the global pixel correlation. By this, the proposed Inception Transformer learns the long‐distance pixel dependency between the cover and secret images. Furthermore, we propose a ‘Skip Connection’ mechanism in the proposed Inception Transformer, which merges the low‐level visual features and high‐level semantic features and achieves better model performance. In detail, The HiiT generates higher‐quality stego images with 45.46 PSNR and 0.9915 SSIM. Besides, accurately restored secret images achieve 47.27 PSNR and 0.9952 SSIM. Extensive experimental results show the proposed HiiT significantly improves the image‐hiding performance compared with state‐of‐the‐art methods.

Specular highlight removal using Quaternion transformer

Specular highlight removal is a very important issue, because specular highlight reflections in images with illumination changes can give very negative effects on various computer vision and image processing tasks. Numerous state-of-the-art networks for the specular removal use convolutional neural networks (CNN), which cannot learn global context effectively. They capture spatial information while overlooking 3D structural correlation information of an RGB image. To address this problem, we introduce a specular highlight removal network based on Quaternion transformer (QformerSHR), which employs a transformer architecture based on Quaternion representation. In particular, a depth-wise separable Quaternion convolutional layer (DSQConv) is proposed to enhance computational performance of QformerSHR, while efficiently preserving the structural correlation of an RGB image by utilizing the Quaternion representation. In addition, a Quaternion transformer block (QTB) based on DSQConv learns global context. As a result, QformerSHR consisting of DSQConv and QTB performs the specular removal from natural and text image datasets effectively. Experimental results demonstrate that it is significantly more effective than state-of-the-art networks for the specular removal, in terms of both quantitative performance and subjective quality.

LGIT: local–global interaction transformer for low-light image denoising

Transformer-based methods effectively capture global dependencies in images, demonstrating outstanding performance in multiple visual tasks. However, existing Transformers cannot effectively denoise large noisy images captured under low-light conditions owing to (1) the global self-attention mechanism causing high computational complexity in the spatial dimension owing to a quadratic increase in computation with the number of tokens; (2) the channel-wise self-attention computation unable to optimise the spatial correlations in images. We propose a local–global interaction Transformer (LGIT) that employs an adaptive strategy to select relevant patches for global interaction, achieving low computational complexity in global self-attention computation. A top-N patch cross-attention model (TPCA) is designed based on superpixel segmentation guidance. TPCA selects top-N patches most similar to the target image patch and applies cross attention to aggregate information from them into the target patch, effectively enhancing the utilisation of the image’s nonlocal self-similarity. A mixed-scale dual-gated feedforward network (MDGFF) is introduced for the effective extraction of multiscale local correlations. TPCA and MDGFF were combined to construct a hierarchical encoder-decoder network, LGIT, to compute self-attention within and across patches at different scales. Extensive experiments using real-world image-denoising datasets demonstrated that LGIT outperformed state-of-the-art (SOTA) convolutional neural network (CNN) and Transformer-based methods in qualitative and quantitative results.

Collaborative masking based speckle disentanglement for self-supervised optical coherence tomography image despeckling

Optical coherence tomography (OCT) has proven to be an effective and safe diagnostic tool in clinical settings due to its unique advantages. Nonetheless, the OCT images are unavoidably corrupted with speckle noise. Despeckling has become a vital preliminary step in OCT based image processing and analysis tasks. The supervised deep learning (DL) based denoising algorithms have recently attracted much attention due to their promising performance, but the lack of the noise-free OCT images renders it difficult to provide the effective supervision for DL model training. This paper has presented a self-supervised OCT image despeckling approach which does not depend on the reference clean image and combines the disentangled representation strategy with speckle noise estimation for the effective denoising. The proposed algorithm firstly disentangles the corrupted image into the speckle noise and clean image. Then, the multiple generated clean images are multiplied by the disentangled speckle noise to synthesize the speckle-corrupted image for self-supervised learning. To make the generated clean image and extracted speckle noise more statistically realistic, a collaborative masking strategy and a noise calibration module are applied to explore the spatial correlation of the clean image and refine speckle noise. The proposed method has been validated on two public datasets. Experimental results demonstrate that our method has superior speckle reduction performance to some traditional and DL-based despeckling approaches in terms of visual evaluation and quantitative metrics. Particularly, our method provides contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) improvements of at least 0.35 dB and 14.37 dB over the compared methods, respectively. Additionally, the retinal image classification results provide further evidence for the exceptional despeckling performance of our approach against other compared methods. It is obvious that the developed method has significant advantages in keeping the fidelity of retinal structure and enhancing the speckle suppression level. Meanwhile, the presented method holds certain reference value for improving diagnostic efficiency in clinical.

DNTFE-Net: Distant Neighboring-Temporal Feature Enhancement Network for side scan sonar small object detection

Side-scan sonar small target detection becomes fundamental work for side-scan sonar applications which holds vital importance for marine engineering, maritime military, and so on. However, existing algorithms suffer from performance degradation and poor portability when sonar data is limited. We find that benefit from the strong temporal correlation of sonar images, background information from the long-term images and foreground information from the short-term images can be used to enhance small targets in the current image. To fully utilize these two cues, this paper presents the Distant Neighboring-Temporal Feature Enhancement Network (DNTFE-Net) for detecting small objects in sonar images. The algorithm is based on the signal acquisition process of sonar devices, which is considered a continuous time sequence. Firstly, a flexible foreground feature alignment module is developed to align the foreground information between neighboring signals and the current signal so that foreground information can be used to enhance small target features. Then, we propose a background comparison module for selecting distant-temporal signals that provide the most contextual information in order to improve the ability of migrating to different domains. Finally, a foreground background feature amalgamation module is designed to fuse neighboring and distant-temporal signals for enhancing small target features. Furthermore, real sonar data is collected to compose the real continuous sonar dataset. Our DNTFE-Net exhibits superior recognition performance on the real sonar dataset compared to other popular methods, with an improvement of at least 27%. Our algorithms are publicly available at https://github.com/zbyhnu/DNTFE-Net.git.

Designing five-dimensional non-degeneracy chaotic system and its application in reversible data hiding

The application of privacy protection techniques combining chaotic systems with data hiding has attracted considerable attention in recent years. However, the existing schemes have been found to be defective in terms of security and embedding capacity. This paper proposes a construction method for a 5-dimensional non-degeneracy chaotic system (5D-NDCS) to address the shortcomings of the previous approach. The 5D-NDCS possesses five positive Lyapunov exponents, and each dimension can generate chaotic sequences with strong randomness. Utilizing the pseudo-random sequence generated by the 5D-NDCS to encrypt the compressed image can enhance the security of the encrypted image. Subsequently, this paper introduces a joint bit-plane compression (JBPC) algorithm, which is based on the chaotic system and pixel value prediction technique. This algorithm effectively utilizes the pixel correlation of the original image to compress the bit sequence sufficiently, thereby reserving the hiding space. Finally, this paper proposes a reversible data hiding in encrypted images (RDHEI) scheme that supports multiple data hiders, based on the JBPC algorithm. The experimental results demonstrate the scheme’s advantages in terms of embedding rate, security, and fault tolerance.

Gca-pvt-net: group convolutional attention and PVT dual-branch network for oracle bone drill chisel segmentation

Oracle bones (Obs) are a significant carrier of the shang dynasty civilization, primarily consisting of tortoise shells and animal bones, through the study of which we can gain a deeper understanding of the political, economic, religious, and cultural aspects of the shang dynasty. The oracle bone drill chisel (Obdc) is considered an essential non-textual material. The segmentation of Obdc assists archaeologists determine the approximate age of the Obs, which possesses considerable research value. However, the breakage of thousands of years of underground buried Obs, the blurring of the edges of the area burned by the Obdc, the different shapes, and the inconsistent number have brought challenges to the accurate segmentation of the Obdc. In this article, we propose a group convolutional attention and pvt dual-branch network (GCA-PVT-Net) for Obdc segmentation. To our knowledge, this paper is the first to research the automatic segmentation of Obdc. It is a hybrid Convolutional neural network (CNN) and Transformer framework. The work offers the following contributions: (1) The Obdc images are labeled based on the delineation criteria of different drill chisel (DC) shapes to create the Obdc dataset. (2) A convolutional attention module (CAM) is proposed as both an encoder and decoder. The feature extraction process, which effectively integrates global and local information, ensures better modeling of long-term correlations in images while preserving details. (3) A channel feature aggregation module (CFAM) is designed to enhance the effective integration of channel features, enabling feature fusion across various branches and at different levels. (4) The edge deep supervision strategy is applied to smooth the jagged edge of the predicted images at the decoder’s end. Extensive experiments on the Obdc dataset show that GCA-PVT-Net outperforms other state-of-the-art (SOTA) methods. The comparative experimental results show that the edge accuracy and segmentation accuracy of the model reach the top 1.

DSCEH: Dual-Stream Correlation-Enhanced Deep Hashing for Image Retrieval

Deep Hashing is widely used for large-scale image-retrieval tasks to speed up the retrieval process. Current deep hashing methods are mainly based on the Convolutional Neural Network (CNN) or Vision Transformer (VIT). They only use the local or global features for low-dimensional mapping and only use the similarity loss function to optimize the correlation between pairwise or triplet images. Therefore, the effectiveness of deep hashing methods is limited. In this paper, we propose a dual-stream correlation-enhanced deep hashing framework (DSCEH), which uses the local and global features of the image for low-dimensional mapping and optimizes the correlation of images from the model architecture. DSCEH consists of two main steps: model training and deep-hash-based retrieval. During the training phase, a dual-network structure comprising CNN and VIT is employed for feature extraction. Subsequently, feature fusion is achieved through a concatenation operation, followed by similarity evaluation based on the class token acquired from VIT to establish edge relationships. The Graph Convolutional Network is then utilized to enhance correlation optimization between images, resulting in the generation of high-quality hash codes. This stage facilitates the development of an optimized hash model for image retrieval. In the retrieval stage, all images within the database and the to-be-retrieved images are initially mapped to hash codes using the aforementioned hash model. The retrieval results are subsequently determined based on the Hamming distance between the hash codes. We conduct experiments on three datasets: CIFAR-10, MSCOCO, and NUSWIDE. Experimental results show the superior performance of DSCEH, which helps with fast and accurate image retrieval.

Joint k-ω Space Image Reconstruction and Data Fitting for Chemical Exchange Saturation Transfer Magnetic Resonance Imaging.

Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) is a novel MRI technology to image certain compounds at extremely low concentrations. Long acquisition time to measure signals at a set of offset frequencies of the Z-spectra and to repeat measurements to reduce noise pose significant challenges to its applications. This study explores correlations of CEST MR images along the spatial and Z-spectral dimensions to improve MR image quality and robustness of magnetization transfer ratio (MTR) asymmetry estimation via a joint k-ω reconstruction model. The model was formulated as an optimization problem with respect to MR images at all frequencies ω, while incorporating regularizations along the spatial and spectral dimensions. The solution was subject to a self-consistency condition that the Z-spectrum of each pixel follows a multi-peak data fitting model corresponding to different CEST pools. The optimization problem was solved using the alternating direction method of multipliers. The proposed joint reconstruction method was evaluated on a simulated CEST MRI phantom and semi-experimentally on choline and iopamidol phantoms with added Gaussian noise of various levels. Results demonstrated that the joint reconstruction method was more tolerable to noise and reduction in number of offset frequencies by improving signal-to-noise ratio (SNR) of the reconstructed images and reducing uncertainty in MTR asymmetry estimation. In the choline and iopamidol phantom cases with 10.5% noise in the measurement data, our method achieved an averaged SNR of 31.0 dB and 32.2 dB compared to the SNR of 24.7 dB and 24.4 dB in the conventional reconstruction approach. It reduced uncertainty of the MTR asymmetry estimation over all regions of interest by 54.4% and 43.7%, from 1.71 and 2.38 to 0.78 and 1.71, respectively.

Evaluation of the Technical Condition of Pipes during the Transportation of Hydrogen Mixtures According to the Energy Approach

In this study, a theoretical–experimental methodology for determining the stress–strain state in pipeline systems, taking into account the hydrogen environment, was developed. A complex of theoretical and experimental studies was conducted to determine the specific energy of destruction as an invariant characteristic of the material’s resistance to strain at different hydrogen concentrations. The technique is based on the construction of complete diagrams of the destruction of the material based on the determination of true strains and stresses in the local volume using the method involving the optical–digital correlation of speckle images. A complex of research was carried out, and true diagrams of material destruction were constructed, depending on the previous elastic–plastic strain and the action of the hydrogen environment. The change in the concentration of hydrogen absorbed by the material was estimated, depending on the value of the specific energy of destruction. A study was conducted on tubular samples, and the degree of damage to the material of the inner wall under the action of hydrogen and stress from the internal pressure was evaluated according to the change in specific energy, depending on the value of the true strain established with the help of an optical–digital correlator on the outer surface, and the degree of damage was determined. It has been established that the specific fracture energy of 17G1S steel decreases by 70–90% under the influence of hydrogen. The effect of the change in the amount of strain energy on the thickness of the pipe wall is illustrated.

Activity recognition based on spatio-temporal features with transfer learning

<span lang="EN-US">Human action recognition (HAR) has gathered significant attention recently due to its high demand in various application domains. We explore different CNN architectures for extracting Spatio-Temporal features. In addition to spatial correlation in 2D images, video actions also own the temporal domain attributes. Many research attempts addressed simple activity recognition, but complex activity recognition is still challenging. In the proposed work, the concept of transfer learning consists of two pre-trained networks, ResNet50 and VGG16, which are used to extract Spatiotemporal features and are later fused using the averaging method. Pre-processed frames are considered for the pre-trained network, the pre-processing consists of frame extraction, resizing, and contrast enhancement. The fused features are trained using a multiclass non-linear SVM classifier that achieved an accuracy of 89.71% on the UCF-101 database.</span>

Cross-modal attention network for retinal disease classification based on multi-modal images.

Multi-modal eye disease screening improves diagnostic accuracy by providing lesion information from different sources. However, existing multi-modal automatic diagnosis methods tend to focus on the specificity of modalities and ignore the spatial correlation of images. This paper proposes a novel cross-modal retinal disease diagnosis network (CRD-Net) that digs out the relevant features from modal images aided for multiple retinal disease diagnosis. Specifically, our model introduces a cross-modal attention (CMA) module to query and adaptively pay attention to the relevant features of the lesion in the different modal images. In addition, we also propose multiple loss functions to fuse features with modality correlation and train a multi-modal retinal image classification network to achieve a more accurate diagnosis. Experimental evaluation on three publicly available datasets shows that our CRD-Net outperforms existing single-modal and multi-modal methods, demonstrating its superior performance.

Sparse-View Spectral CT Reconstruction Based on Tensor Decomposition and Total Generalized Variation

Spectral computed tomography (CT)-reconstructed images often exhibit severe noise and artifacts, which compromise the practical application of spectral CT imaging technology. Methods that use tensor dictionary learning (TDL) have shown superior performance, but it is difficult to obtain a high-quality pre-trained global tensor dictionary in practice. In order to resolve this problem, this paper develops an algorithm called tensor decomposition with total generalized variation (TGV) for sparse-view spectral CT reconstruction. In the process of constructing tensor volumes, the proposed algorithm utilizes the non-local similarity feature of images to construct fourth-order tensor volumes and uses Canonical Polyadic (CP) tensor decomposition instead of pre-trained tensor dictionaries to further explore the inter-channel correlation of images. Simultaneously, introducing the TGV regularization term to characterize spatial sparsity features, the use of higher-order derivatives can better adapt to different image structures and noise levels. The proposed objective minimization model has been addressed using the split-Bregman algorithm. To assess the performance of the proposed algorithm, several numerical simulations and actual preclinical mice are studied. The final results demonstrate that the proposed algorithm has an enormous improvement in the quality of spectral CT images when compared to several existing competing algorithms.

Full-scale semantic segmentation of hyperspectral imaging based on spatial spatial-spectral joint network

Abstract. Hyperspectral images contain dozens or even hundreds of spectral bands, which contain rich spectral information and help distinguish different ground objects. Hyperspectral images have a wide range of applications in urban planning, environmental monitoring, and other fields. The semantic segmentation of hyperspectral images is one of the current research hotspots. The difficulty lies in the rich spectral information and strong correlation of hyperspectral images. Traditional semantic segmentation methods cannot fully extract information, which affects the accuracy of classification. This article utilizes an encoding decoding structure to simultaneously extract deep and shallow features of images. A REGCS convolution module was constructed using the idea of group convolution to extract spectral and spatial features of images. We compared the Salinas Valley dataset and MUUFL dataset with various classification algorithms. The experimental results show that compared with other classification models, the RESSU model has achieved stable and excellent results in hyperspectral image classification experiments. Among them, in the classification experiment of the Salinas Valley dataset, the accuracy of single class classification reached over 92%. In the effectiveness analysis experiment, we calculated different model parameter quantities to verify the performance of our method, and ultimately achieved good results.

Investigation of the speckle pattern effect for displacement assessments by DIC

PurposeThis study aims to investigate the effect of speckle pattern on displacement measurements using different speckle diameters and coverage ratios.Design/methodology/approachIn order to compare the coverage ratio and speckle diameter during the evaluation of the correlation of digital images (DIC) study, template speckle plates were produced on a computer numerical control (CNC) punch press with 600 punches per minute. After the speckle plates were manufactured, the speckled pattern was randomly painted on a plain white side through the manufactured template plates, and then tensile tests were performed under the same loading conditions for each sample to observe displacement variation via correlation parameters.FindingsDuring the manufacturing of templates with thin plates, a punch diameter of less than 1.7 mm will cause tool failure; therefore, uniform speckle size can be assessed before operation. A higher coverage ratio resulted in more accurate and reliable results in displacement data. With smaller coverage, the facet size should be increased to achieve favorable results.Research limitations/implicationsIf thick template plates are selected, speckle painting cannot be done properly; therefore, template thickness shall also be assessed before operation.Practical implicationsFor randomly distributed DIC templates, increasing coverage beyond 50% does not make sense due to difficulties in the production process in the punch press.Originality/valueEvaluating DIC results via templates manufactured in a punch press with different speckle diameters and coverage ratios is a new topic in literature.