Increase Image Resolution Research Articles

Modelling the impact of single vs. dual presentation on visual discrimination across resolutions.

Visual categorisation relies on our ability to extract useful diagnostic information from complex stimuli. To do this, we can utilise both the "high-level" and "low-level" information in a stimulus; however, the extent to which changes in these properties impact the decision-making process is less clear. We manipulated participants' access to high-level category features via gradated reductions to image resolution while exploring the impact of access to additional category features through a dual-stimulus presentation when compared with single stimulus presentation. Results showed that while increasing image resolution consistently resulted in better choice performance, no benefit was found for dual presentation over single presentation, despite responses for dual presentation being slower compared with single presentation. Applying the diffusion decision model revealed increases in drift rate as a function of resolution, but no change in drift rate for single versus dual presentation. The increase in response time for dual presentation was instead accounted for by an increase in response caution for dual presentations. These findings suggest that while increasing access to high-level features (via increased resolution) can improve participants' categorisation performance, increasing access to both high- and low-level features (via an additional stimulus) does not.

Enhancing consistency in virtual try-on: A novel diffusion-based approach

In new technology scenarios, virtual try-on aims to integrate clothing onto body images naturally, enhancing shopping experience by simulating true effect of clothes. As the image resolution increases, we expect to improve the consistency of the result, i.e., to ensure that various elements of the image are harmonized in terms of color, shading, style, and texture in order to achieve a natural visual effect. Many studies based on Generative Adversarial Networks(GANs) struggle with consistency. They encounter challenges in accurately depicting the fabric of target garments, as well as natural shadows and folds, and sometimes exhibit visual discontinuities or inconsistencies. So, we propose a new approach CSD-VTON based on latent diffusion model. Considering that traditional UNet's computational primitives struggle to capture complex transformation relationships at the pixel level, we address this issue by concatenating the warped cloth images generated by the warping module with the noise image. Additionally, cascade feature extraction module is introduced to extract in-store garment features, which ensures the preservation of texture and details in the target garments. Finally, we incorporate the skip-connection supplementary module to compensate for the reconstruction error. We conducted experiments using the DressCode and VITON-HD datasets to demonstrate the effectiveness and superiority of our approach.

Attention-based digital filter with anchor-free feature pyramid learning model for pedestrian detection

Ensuring real-time performance while leveraging pedestrian detection is a crucial prerequisite for intelligent driving technology. The development of lightweight models with good detection accuracy is also crucial. This work proposes a novel method, the Attention Digital Filter with Anchor-Free Feature Pyramid Learning Model (ADFAFPLM), to meet these needs. The suggested method consists of combining two networks: one is a digital filter based on an attention network that eliminates noise and other picture distortions. The attention-based residual network digital filters are chosen for their enhanced filtering performance, adaptability, efficient learning through residual connections, noise suppression, interpretability, and generalization capabilities. Next, from the input crowded and occluded photos, the pedestrian is identified using an anchor-free feature pyramid network. The Eurocity person dataset was used to train the model, and it was also tested on other datasets like CityPersons, INRIA, PennFudan, and Eurocity. The investigation was expanded to include images in hazy, noisy, and occlusion environments, among other environmental conditions. The image resolutions were also considered for analysis and it was observed that with increasing image resolution, the mAP increases. Based on the ablation study, the ADF-AFPLM adopted YOLOv8n with batch size 16, and image size 640 is considered for efficient result with different testing datasets. The model achieved a mean average precision (mAP) of approx. 87% and shows its efficacy over state-of-art models.

A Novel Framework of Hybrid Optimization Techniques for Contrast-Enhancement in Cardiac MRI Medical Images

Medical image processing plays a crucial role in diagnosing and treating various diseases, with Magnetic Resonance Imaging (MRI) being a widely used modality. A previous study introduced an algorithm aimed at eliminating noise from MRI images through a filtering process, effectively suppressing mixed noise while preserving important structures like edges. However, limitations in optimizing parameters for image enhancement and occasional edge preservation failures were observed. Addressing the challenge of inadequate contrast enhancement in medical image processing, this study proposes a solution utilizing the hybrid cetacean optimization algorithm and Sand Cat Swarm Optimization (COA-SCSO) to enhance contrast in MRI images. The proposed approach focuses on improving the contrast nature in MRI images to mitigate image degradation during acquisition. Cardiac MRI images from SCMR consensus and AMRG Atlas databases are analyzed, emphasizing the consideration of spatial information for enhancement. The enhancement process involves integrating a cost function with a contrast measure based on multiple metrics to create a novel transformation function. Soft-computing approaches are employed to reduce time complexity, while expanding the search pattern through a transformation function in the spatial domain. This method enhances pixel intensity, contributing to increased image resolution. Performance evaluation of the proposed algorithm incorporates various parameters such as Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Normalized Absolute Error (NAE), and Structural Similarity Index (SSIM). Results from analysis of ten cardiac MRI images indicate that the proposed algorithm achieves the highest PSNR (98) and SSIM (0.99) values, as well as the lowest NAE (-0.17) and MSE (0.16) values, compared to the particle swarm optimized texture-based histogram equalization (PSOTHE) method and modified sunflower optimization (MSFO) method. Overall, the COA-SCSO approach demonstrates promising results in enhancing contrast in MRI images, offering potential benefits for medical diagnosis and treatment planning. Further research could explore additional optimization techniques and validation on larger datasets to further validate its effectiveness in clinical practice.

Assessing left atrial size and pump function in ischemic stroke patients: Is cardiac MRI superior to transthoracic echocardiography?

ObjectivesCurrent guidelines recommend transthoracic echocardiography (TTE) following an ischemic stroke as the primary technique to identify cardiac abnormalities associated with an increased risk of cerebral embolism. It is unclear whether cardiac magnetic resonance imaging (cMRI), a technique shown to provide increased imaging resolution, may also enhance the cardiac assessment of ischemic stroke patients. We compared cMRI with TTE in the evaluation of Left Atrial (LA) size and pump function in a cohort of 44 patients with ischemic stroke. Materials and methodsThe biplane method was utilized to acquire LA diameters as well as area measurements in both TTE and cMRI. We calculated LA volume (LAV), LAV index (LAVI), LA Global Longitudinal Strain (GLS) and LA pump function. Results were compared using paired two sample for means t-test. Lin's concordance correlation coefficient (CCC) and Bland-Altman methods quantified the agreement of measurements obtained by TTE and cMRI. ResultsLAVI measurements by cMRI were significantly larger (34.97 v. 28.81; p = 0.001) than by TTE. The concordance correlation demonstrated only a weak agreement between LA size measured by cMRI and TTE (ρc = 0.397; p= 0.001, 95% CI 0.16 - 0.59), and the Bland-Altman plot demonstrated that LAVI measured by cMRI averaged 6.3 ml/m2 larger magnitude than those obtained by TTE. ConclusionsUsing TTE alone leads to an underestimation of LA abnormalities important in the evaluation of ischemic stroke patients. Nearly one in every five ischemic stroke patients evaluated based on the current guidelines may have a missed potential source of cardiac embolism.

Parallel computational ghost imaging with modulation patterns multiplexing and permutation inspired by compound eyes

Real-time computational ghost imaging (CGI) has received significant attention in recent years to overcome the trade-off between long acquisition time and high reconstructed image quality of CGI. Inspired by compound eyes, we propose a parallel computational ghost imaging with modulation patterns multiplexing and permutation to achieve a faster and high-resolution CGI. With modulation patterns multiplexing and permutation, several small overlapping fields-of-view can be obtained; meanwhile, the difficulty in alignment of illumination light field and multiple detectors can be well resolved. The method combining compound eyes with multi-detectors to capture light intensity can resolve the issue of a gap between detector units in the array detector. Parallel computation facilitates significantly reduced acquisition time, while maintaining reconstructed quality without compromising the sampling ratio. Experiments indicate that using m × m detectors reduce modulation pattern count, projector storage, and projection time to around 1/m2 of typical CGI methods, while increasing image resolution to m2 times. This work greatly promotes the practicability of parallel computational ghost imaging and provides optional solution for real-time computational ghost imaging.

Image Super-Resolution Reconstruction Model Based on Multi-Feature Fusion

Due to the limitations of imaging equipment and image transmission conditions on daily image acquisition, the images acquired are usually low-resolution images, and it will cost a lot of time and economic costs to increase image resolution by upgrading hardware equipment. In this paper, we propose an image super-resolution reconstruction algorithm based on spatio-temporal-dependent residual network MSRN, which fuses multiple features. The algorithm uses the surface feature extraction module to extract the input features of the image, and then uses the deep residual aggregation module to adaptively learn the deep features, and then fuses multiple features and learns the global residual. Finally, the high-resolution image is obtained through the up-sampling module and the reconstruction module. In the model structure, different convolution kernels and jump connections are used to extract more high-frequency information, and spatio-temporal attention mechanism is introduced to focus on more image details. The experimental results show that compared with SRGAN, VDSR and Laplacian Pyramid SRN, the proposed algorithm finally achieves better reconstruction effect, and the image texture details are clearer under different scaling factors. In objective evaluation, the peak signal-to-noise ratio (PSNR) and structure similarity (SSIM) of the proposed algorithm are improved compared with SRGAN.

Development of a low-temperature immersion microscopy technique for ice research

Abstract Perennial ice can be studied for many purposes, including paleoclimate records or rheological properties. For most of those purposes, the ice microstructure must be studied, often through optical microscopy. The aim of this work is to assess the viability of immersion microscopy for the study of ice microstructures. It consists of using an oil between the objective lens and the specimen, to increase image resolution. Immersion microscopy is a technique well-developed for the investigation of diverse materials, but it has so far not been explored for ice research. Here we investigate the challenges and advantages of that technique. The main challenge is related to the selection of the immersion oil itself, which must satisfy a number of criteria, ranging from refractive index and viscosity to toxicity and reactivity. We identify pure silicone oil (dimethicone) as a simple and safe option for immersion microscopy of inner ice structures. Among its advantages, it provides higher resolution (compared to standard ‘dry’ microscopy) and it can be simultaneously used as a long-term coating to prevent undesired sublimation of the ice-sample surfaces. For the observation of surface structures, however, another type of oil with higher refractive index should be used.

Analysis of the Benefits of X-Ray Waves in Ultrasound: Literature Review

In the information age like now, waves are very influential on human life. Almost all living creatures on earth are inseparable from the use of waves. One of them is X-ray and ultrasound waves. The use of X-rays and ultrasonic (USG) waves in various fields, especially in the health sector, is an important aspect of technological development. As well as its application in the medical and industrial fields. Electromagnetic waves, especially X-rays, have become an irreplaceable diagnostic instrument in visualizing the internal structure of the human body. The various benefits of X-rays are explained, from diagnosing bone fractures to radiotherapy treatment for cancer. On the other hand, ultrasound is used for pregnancy checks, monitoring the condition of the fetus, and detecting tumors and other disorders. Although ultrasound does not use direct X-ray waves, the integration of X-ray processing technology can increase image resolution and provide more comprehensive information. In this article, we will discuss several benefits of waves, namely X-ray and ultrasound waves. X-rays use electromagnetic waves with a higher frequency that are ionizing. Meanwhile, ultrasound uses ultrasonic waves with non-ionization properties. The use of X-ray and ultrasound waves includes the medical and industrial fields. The research method uses literature studies by collecting and analyzing information from various credible sources obtained from 15 journals and 3 books. The results of the literature analysis include definitions, frequencies, and wavelengths, as well as the benefits and properties of X-rays and ultrasound. Although both are rarely used, some techniques combine them to increase image resolution and clarity.

Impact on Reconstruction SPECT Image Distortion by Iteration and Subset Number

Iterative reconstruction on Single Photon Emission Computed Tomography (SPECT) imaging has recently been commercially available, resulting in reconstructed images with lower noise levels and better spatial resolution. The reconstruction variables employed and chosen under these suggestions include post-filtering, as well as several iterations and subset numbers. However, most of the number of iterations and subsets will give distortion to the segmentation image on a volume to be segmented. For this purpose, this paper will compare the effect of distortion on the dice similarity coefficient of volume segmentation for the iterative reconstruction setting by default as practice. The best outcome with the smallest root-mean-square deviation, highest percentage contrast values, and highest dice similarity coefficient) for all iterations and subsets is in iteration 6 subset 16. Here, the root-mean-square deviation is 8.06. Moreover, the four most enormous spheres can be calculated from the six spheres filled. The best contrast percentage is in subset 32, but the background variability for that subset is exceptionally high, which is 64.5. With post-filtering implementation, the scatter-corrected slices show the most enhanced image resolution and contrast when using advanced Astonish reconstruction algorithms. The user must decide if noise reduction via post-filtering or increased image resolution is more suitable for a certain imaging process.

A Rapid Parallel Mosaicking Algorithm for Massive Remote Sensing Images Utilizing Read Filtering

Mosaicking is a crucial step in the application of remote sensing images. The amount of remote sensing image data has grown rapidly, along with the expansion of observed areas and increased image resolution. As a result, traditional serial mosaicking techniques are facing significant challenges. In recent times, various studies have utilized high-performance computing to hasten image mosaicking and attain favorable outcomes. Nevertheless, the current research only accelerates mosaicking through external technology, without optimizing from the perspective of algorithm flow, which introduces unnecessary data I/O and slows down the mosaicking. This paper introduces a rapid parallel remote sensing image mosaicking algorithm utilizing read filtering. To begin with, the target images are divided into blocks and stored in a distributed file system. Subsequently, the image blocks are read and filtered based on a designated input format. Finally, the overlapping and non-overlapping areas are read and processed asynchronously, reducing the data I/O and computing overhead, thereby improving the efficiency of parallel computing. The experiments indicate that the mosaicking algorithm introduced in this paper enhances throughput and speedup by an average of 1.38 MB/S and 0.87 relative to the current techniques, respectively, concerning various datasets and cores. This study provides a theoretical foundation and novel ideas for processing remote sensing images on cluster platforms.

Structured low-rank reconstruction for navigator-free water/fat separated multi-shot diffusion-weighted EPI.

Multi-shot diffusion-weighted EPI allows an increase in image resolution and reduced geometric distortions and can be combined with chemical-shift encoding (Dixon) to separate water/fat signals. However, such approaches suffer from physiological motion-induced shot-to-shot phase variations. In this work, a structured low-rank-based navigator-free algorithm is proposed to address the challenge of simultaneously separating water/fat signals and correcting for physiological motion-induced shot-to-shot phase variations in multi-shot EPI-based diffusion-weighted MRI. We propose an iterative, model-based reconstruction pipeline that applies structured low-rank regularization to estimate and eliminate the shot-to-shot phase variations in a data-driven way, while separating water/fat images. The algorithm is tested in different anatomies, including head-neck, knee, brain, and prostate. The performance is validated in simulations and in-vivo experiments in comparison to existing approaches. In-vivo experiments and simulations demonstrated the effectiveness of the proposed algorithm compared to extra-navigated and an alternative self-navigation approach. The proposed algorithm demonstrates the capability to reconstruct in the multi-shot/Dixon hybrid space domain under-sampled datasets, using the same number of acquired EPI shots compared to conventional fat-suppression techniques but eliminating fat signals through chemical-shift encoding. In addition, partial Fourier reconstruction can also be achieved by using the concept of virtual conjugate coils in conjunction with the proposed algorithm. The proposed algorithm effectively eliminates the shot-to-shot phase variations and separates water/fat images, making it a promising solution for future DWI on different anatomies.

Enhancing obSTORM imaging performance with cubic spline PSF modeling.

Oblique plane microscopy-based single molecule localization microscopy (obSTORM) has shown great potential for super-resolution imaging of thick biological specimens. Despite its compatibility with tissues and small animals, prior uses of the Gaussian point spread function (PSF) model have resulted in limited imaging resolution and a narrow axial localization range. This is due to the poor fit of the Gaussian PSF model with the actual PSF shapes in obSTORM. To overcome these limitations, we have employed cubic splines for a more accurate modeling of the experimental PSF shapes. This refined PSF model enhances three-dimensional localization precision, leading to significant improvements in obSTORM imaging of mouse retina tissues, such as an approximately 1.2 times increase in imaging resolution, seamless stitching of single molecules between adjacent optical sections, and a doubling of the sectional interval in volumetric obSTORM imaging due to the extended axial range of usable section thickness. The cubic spline PSF model thus offers a path towards more accurate and faster volumetric obSTORM imaging of biological specimens.

Remote sensing building damage assessment with a multihead neighbourhood attention transformer

ABSTRACT Most existing remote sensing disaster assessment methods rely on convolutional neural networks (CNNs). Although CNNs can extract effective semantic features, determining global spatial relationships remains limited due to the locality of convolutional operations. The recently developed transformer-based method can extract global information from images effectively by encoding image tokens. However, its consumption of computational resources varies markedly with increasing image resolution. In this paper, we propose a novel transformer-based neural network for disaster assessment problems. The network uses a multihead neighbourhood attention (MNA) transformer as the base layer of the encoder to achieve more efficient self-attention computation. In addition, the bitemporal feature fusion module (BFFM) performs differential enhancement and injects the change information to the decoder via skip connections. The multiscale tokenizer generates multiscale image tokens to mitigate the loss of detail during encoding. Experimental results on three datasets show that the proposed method outperforms existing methods.

Deep learning discrimination of rheumatoid arthritis from osteoarthritis on hand radiography.

To develop a deep learning model to distinguish rheumatoid arthritis (RA) from osteoarthritis (OA) using hand radiographs and to evaluate the effects of changing pretraining and training parameters on model performance. A convolutional neural network was retrospectively trained on 9714 hand radiograph exams from 8387 patients obtained from 2017 to 2021 at seven hospitals within an integrated healthcare network. Performance was assessed using an independent test set of 250 exams from 146 patients. Binary discriminatory capacity (no arthritis versus arthritis; RA versus not RA) and three-way classification (no arthritis versus OA versus RA) were evaluated. The effects of additional pretraining using musculoskeletal radiographs, using all views as opposed to only the posteroanterior view, and varying image resolution on model performance were also investigated. Area under the receiver operating characteristic curve (AUC) and Cohen's kappa coefficient were used to evaluate diagnostic performance. For no arthritis versus arthritis, the model achieved an AUC of 0.975 (95% CI: 0.957, 0.989). For RA versus not RA, the model achieved an AUC of 0.955 (95% CI: 0.919, 0.983). For three-way classification, the model achieved a kappa of 0.806 (95% CI: 0.742, 0.866) and accuracy of 87.2% (95% CI: 83.2%, 91.2%) on the test set. Increasing image resolution increased performance up to 1024 × 1024 pixels. Additional pretraining on musculoskeletal radiographs and using all views did not significantly affect performance. A deep learning model can be used to distinguish no arthritis, OA, and RA on hand radiographs with high performance.

Lightweight semantic image synthesis with mutable framework

Image synthesis is a critical task in various computer vision technologies, and lots of methods tried to translate semantic images into realistic ones for controllable synthesis. With the increasing image resolution, networks are becoming larger, and applications of related methods are restricted. To alleviate the problem, we propose a lightweight mutable network for semantic image synthesis. The network is based on generative adversarial networks. We introduce the feature pyramid architecture to the generator and reduce the hidden node numbers. We also design a mutable scheme where the pyramid will involve fewer layers for smaller images. To improve the performance of the lightweight generator, we further propose a weighted discriminator and a refined loss. The experiments on several public datasets show that our method is effective and achieves competitive performance, proving that a small network can also achieve high-quality semantic image synthesis.

Extraction of fractures in shale CT images using improved U-Net

Accurate extraction of pores and fractures is a prerequisite for constructing digital rocks for physical property simulation and microstructural response analysis. However, fractures in CT images are similar in grayscale to the rock matrix, and traditional algorithms have difficulty to achieve accurate segmentation results. In this study, a dataset containing multiscale fracture information was constructed, and a U-Net semantic segmentation model with a scSE attention mechanism was used to classify shale CT images at the pixel level and compare the results with traditional methods. The results showed that the CLAHE algorithm effectively removed noise and enhanced the fracture information in the dark parts, which is beneficial for further fracture extraction. The Canny edge detection algorithm had significant false positives and failed to recognize the internal information of the fractures. The Otsu algorithm only extracted fractures with a significant difference from the background and was not sensitive enough for fine fractures. The MEF algorithm enhanced the edge information of the fractures and was also sensitive to fine fractures, but it overestimated the aperture of the fractures. The U-Net was able to identify almost all fractures with good continuity, with an MIou and Recall of 0.80 and 0.82, respectively. As the image resolution increases, more fine fracture information can be extracted.

Reliability of Retinal Layer Annotation with a Novel, High-Resolution Optical Coherence Tomography Device: A Comparative Study.

Optical coherence tomography (OCT) enables in vivo diagnostics of individual retinal layers in the living human eye. However, improved imaging resolution could aid diagnosis and monitoring of retinal diseases and identify potential new imaging biomarkers. The investigational high-resolution OCT platform (High-Res OCT; 853 nm central wavelength, 3 µm axial-resolution) has an improved axial resolution by shifting the central wavelength and increasing the light source bandwidth compared to a conventional OCT device (880 nm central wavelength, 7 µm axial-resolution). To assess the possible benefit of a higher resolution, we compared the retest reliability of retinal layer annotation from conventional and High-Res OCT, evaluated the use of High-Res OCT in patients with age-related macular degeneration (AMD), and assessed differences of both devices on subjective image quality. Thirty eyes of 30 patients with early/intermediate AMD (iAMD; mean age 75 ± 8 years) and 30 eyes of 30 age-similar subjects without macular changes (62 ± 17 years) underwent identical OCT imaging on both devices. Inter- and intra-reader reliability were analyzed for manual retinal layer annotation using EyeLab. Central OCT B-scans were graded for image quality by two graders and a mean-opinion-score (MOS) was formed and evaluated. Inter- and intra-reader reliability were higher for High-Res OCT (greatest benefit for inter-reader reliability: ganglion cell layer; for intra-reader reliability: retinal nerve fiber layer). High-Res OCT was significantly associated with an improved MOS (MOS 9/8, Z-value = 5.4, p < 0.01) mainly due to improved subjective resolution (9/7, Z-Value 6.2, p < 0.01). The retinal pigment epithelium drusen complex showed a trend towards improved retest reliability in High-Res OCT in iAMD eyes but without statistical significance. Improved axial resolution of the High-Res OCT benefits retest reliability of retinal layer annotation and improves perceived image quality and resolution. Automated image analysis algorithms could also benefit from the increased image resolution.

Aperture–patch sandwich metasurface for magnetic field enhancement in 1.5 T MRI

Magnetic resonance imaging (MRI) is an increasingly popular non-invasive technique for clinical diagnosis. Signal-to-noise ratio (SNR) is a crucial performance metric of MRI, improvement of which can be exchanged for increased image resolution or decreased scan time. Besides the progress in various hardware and software techniques for improving SNR in MRI scanners, use of metasurfaces as accessories has recently shown potential towards enhancing SNR by boosting local magnetic field in the scanned volume. Magnetic field enhancement over a larger depth from the skin is essential for imaging of deeper tissues, which can be facilitated by a specifically designed metasurface. Here we present such a metasurface with complementary-type resonant structures on the two sides of a high-permittivity dielectric, which substantially increases magnetic flux density on the skin (forty-five fold) that decays down to unity at a depth of 95 mm from the skin. This results in boosting of SNR up to forty-fold on the skin in 1.5 T MRI, while keeping tissue heating below the safety limit. An original analytical approach is formulated to readily estimate the SNR enhancement factor of this metasurface. Using the designed metasurface as an accessory for MRI scanners could help making MRI scans more efficient and affordable.

Slurry Conditions for Reaction-Induced Slurry-Assisted Grinding of Optical Glass Lens

The demand for optical glass lenses is rising owing to the increase in image resolution. Optical glass is a hard and brittle material. Thus, an efficient and precise grinding method is required for optical glass to improve lens quality and productivity. There are a few methods of producing crack-free machined surfaces; however, they provide only limited grinding efficiency. To resolve this issue, the authors’ group has proposed the reaction-induced slurry-assisted (RISA) grinding method, which expands the range of ductile-regime grinding by utilizing the chemical–mechanical action of a cerium oxide slurry. In this study, the grinding performance of RISA grinding is experimentally evaluated for different pH levels. The results are compared using Tukey’s test, where surface roughness is considered as the characteristic value and the pH value as the analyzed factor. The result shows that RISA grinding efficiently produces a high-quality surface when the slurry is alkaline. The adhesion of cerium oxide abrasives to the wheel in RISA grinding follows the same mechanism as slurry aggregation. In addition, adhesion is more likely to occur when the alkalization of the slurry promotes aggregation. The tank in the slurry supply unit is replaced with a rotating tank to ensure stable RISA grinding with a highly aggregable slurry while preventing aggregation. The performance evaluation shows that a high-quality surface with a surface roughness of less than 10 nm in most parts is obtained. Moreover, the critical depth of cut stably increases by a factor of 5.8.