Saturation Artifacts Research Articles

Dynamic contrast enhanced MRI of the head and neck region using a VIBE sequence with Cartesian undersampling and compressed sensing

ObjectivesCompressed sensing allows for image reconstruction from sparsely sampled k-space data, which is particularly useful in dynamic contrast enhanced MRI (DCE-MRI). The aim of the study was to assess the diagnostic value of a volume-interpolated 3D T1-weighted spoiled gradient-echo sequence with variable density Cartesian undersampling and compressed sensing (CS) for head and neck MRI. MethodsSeventy-one patients with clinical indications for head and neck MRI were included in this study. DCE-MRI was performed at 3 Tesla magnet using CS-VIBE (variable density undersampling, temporal resolution 3.4 s, slice thickness 1 mm). Image quality was compared to standard Cartesian VIBE. Three experienced readers independently evaluated image quality and lesion conspicuity on a 5-point Likert scale and determined the DCE-derived time intensity curve (TIC) types. ResultsCS-VIBE demonstrated higher image quality scores compared to standard VIBE with respect to overall image quality (4.3 ± 0.6 vs. 4.2 ± 0.7, p = 0.682), vessel contour (4.6 ± 0.4 vs. 4.4 ± 0.6, p < 0.001), muscle contour (4.4 ± 0.5 vs. 4.5 ± 0.6, p = 0.302), lesion conspicuity (4.5 ± 0.7 vs. 4.3 ± 0.9, p = 0.024) and showed improved fat saturation (4.8 ± 0.3 vs. 3.8 ± 0.4, p < 0.001) and movement artifacts were significantly reduced (4.6 ± 0.6 vs. 3.7 ± 0.7, p < 0.001). Standard VIBE outperformed CS-VIBE in the delineation of pharyngeal mucosa (4.2 ± 0.5 vs. 4.6 ± 0.6, p < 0.001). Lesion size in cases where a focal lesion was identified was similar for all readers for CS-VIBE and standard VIBE (p = 0.101). TIC curve assessment showed good interobserver agreement (k=0.717). ConclusionCS-VIBE with variable density Cartesian undersampling allows for DCE-MRI of the head and neck region with diagnostic, high image quality and high temporal resolution.

A Saturation Artifacts Inpainting Method Based on Two-Stage GAN for Fluorescence Microscope Images.

Fluorescence microscopic images of cells contain a large number of morphological features that are used as an unbiased source of quantitative information about cell status, through which researchers can extract quantitative information about cells and study the biological phenomena of cells through statistical and analytical analysis. As an important research object of phenotypic analysis, images have a great influence on the research results. Saturation artifacts present in the image result in a loss of grayscale information that does not reveal the true value of fluorescence intensity. From the perspective of data post-processing, we propose a two-stage cell image recovery model based on a generative adversarial network to solve the problem of phenotypic feature loss caused by saturation artifacts. The model is capable of restoring large areas of missing phenotypic features. In the experiment, we adopt the strategy of progressive restoration to improve the robustness of the training effect and add the contextual attention structure to enhance the stability of the restoration effect. We hope to use deep learning methods to mitigate the effects of saturation artifacts to reveal how chemical, genetic, and environmental factors affect cell state, providing an effective tool for studying the field of biological variability and improving image quality in analysis.

ATN-Res2Unet: an advanced deep learning network for the elimination of saturation artifacts in endoscopy optical coherence tomography

Endoscopic optical coherence tomography (OCT) possesses the capability to non-invasively image internal lumens; however, it is susceptible to saturation artifacts arising from robust reflective structures. In this study, we introduce an innovative deep learning network, ATN-Res2Unet, designed to mitigate saturation artifacts in endoscopic OCT images. This is achieved through the integration of multi-scale perception, multi-attention mechanisms, and frequency domain filters. To address the challenge of obtaining ground truth in endoscopic OCT, we propose a method for constructing training data pairs. Experimental in vivo data substantiates the effectiveness of ATN-Res2Unet in reducing diverse artifacts while preserving structural information. Comparative analysis with prior studies reveals a notable enhancement, with average quantitative indicators increasing by 45.4–83.8%. Significantly, this study marks the inaugural exploration of leveraging deep learning to eradicate artifacts from endoscopic OCT images, presenting considerable potential for clinical applications.

Inpainting Saturation Artifact in Anterior Segment Optical Coherence Tomography

The cornea is an important refractive structure in the human eye. The corneal segmentation technique provides valuable information for clinical diagnoses, such as corneal thickness. Non-contact anterior segment optical coherence tomography (AS-OCT) is a prevalent ophthalmic imaging technique that can visualize the anterior and posterior surfaces of the cornea. Nonetheless, during the imaging process, saturation artifacts are commonly generated due to the tangent of the corneal surface at that point, which is normal to the incident light source. This stripe-shaped saturation artifact covers the corneal surface, causing blurring of the corneal edge, reducing the accuracy of corneal segmentation. To settle this matter, an inpainting method that introduces structural similarity and frequency loss is proposed to remove the saturation artifact in AS-OCT images. Specifically, the structural similarity loss reconstructs the corneal structure and restores corneal textural details. The frequency loss combines the spatial domain with the frequency domain to ensure the overall consistency of the image in both domains. Furthermore, the performance of the proposed method in corneal segmentation tasks is evaluated, and the results indicate a significant benefit for subsequent clinical analysis.

Deep learning-based inpainting of saturation artifacts in optical coherence tomography images

Limited by the dynamic range of the detector, saturation artifacts usually occur in optical coherence tomography (OCT) imaging for high scattering media. The available methods are difficult to remove saturation artifacts and restore texture completely in OCT images. We proposed a deep learning-based inpainting method of saturation artifacts in this paper. The generation mechanism of saturation artifacts was analyzed, and experimental and simulated datasets were built based on the mechanism. Enhanced super-resolution generative adversarial networks were trained by the clear–saturated phantom image pairs. The perfect reconstructed results of experimental zebrafish and thyroid OCT images proved its feasibility, strong generalization, and robustness.

A new method for in vivo assessment of corneal transparency using spectral-domain OCT.

Corneal transparency is essential to provide a clear view into and out of the eye, yet clinical means to assess such transparency are extremely limited and usually involve a subjective grading of visible opacities by means of slit-lamp biomicroscopy. Here, we describe an automated algorithm allowing extraction of quantitative corneal transparency parameters with standard clinical spectral-domain optical coherence tomography (SD-OCT). Our algorithm employs a novel pre-processing procedure to standardize SD-OCT image analysis and to numerically correct common instrumental artifacts before extracting mean intensity stromal-depth (z) profiles over a 6-mm-wide corneal area. The z-profiles are analyzed using our previously developed objective method that derives quantitative transparency parameters directly related to the physics of light propagation in tissues. Tissular heterogeneity is quantified by the Birge ratio Br and the photon mean-free path (ls) is determined for homogeneous tissues (i.e., Br~1). SD-OCT images of 83 normal corneas (ages 22-50 years) from a standard SD-OCT device (RTVue-XR Avanti, Optovue Inc.) were processed to establish a normative dataset of transparency values. After confirming stromal homogeneity (Br <10), we measured a median ls of 570 μm (interdecile range: 270-2400 μm). By also considering corneal thicknesses, this may be translated into a median fraction of transmitted (coherent) light Tcoh(stroma) of 51% (interdecile range: 22-83%). Excluding images with central saturation artifact raised our median Tcoh(stroma) to 73% (interdecile range: 34-84%). These transparency values are slightly lower than those previously reported, which we attribute to the detection configuration of SD-OCT with a relatively small and selective acceptance angle. No statistically significant correlation between transparency and age or thickness was found. In conclusion, our algorithm provides robust and quantitative measurements of corneal transparency from standard SD-OCT images with sufficient quality (such as 'Line' and 'CrossLine' B-scan modes without central saturation artifact) and addresses the demand for such an objective means in the clinical setting.

Ultrasound localization microscopy of the human kidney allograft on a clinical ultrasound scanner

Chronic kidney disease is a major medical problem, causing more than a million deaths each year worldwide. Peripheral kidney microvascular damage characterizes most chronic kidney diseases, yet noninvasive and quantitative diagnostic tools to measure this are lacking. Ultrasound Localization Microscopy (ULM) can assess tissue microvasculature with unprecedented resolution. Here, we optimized methods on 35 kidney transplants and studied the feasibility of ULM in seven human kidney allografts with a standard low frame rate ultrasound scanner to access microvascular damage. Interlobar, arcuate, cortical radial vessels, and part of the medullary organization were visible on ULM density maps. The medullary vasa recta can be seen but are not as clear as the cortical vessels. Acquisition parameters were derived from Contrast-Enhanced Ultrasound examinations by increasing the duration of the recorded clip at the same plane. ULM images were compared with Color Doppler, Advanced Dynamic Flow, and Superb Microvascular Imaging with a contrast agent. Despite some additional limitations due to movement and saturation artifacts, ULM identified vessels two to four times thinner compared with Doppler modes. The mean ULM smallest analyzable vessel cross section was 0.3 ± 0.2 mm in the seven patients. Additionally, ULM was able to provide quantitative information on blood velocities in the cortical area. Thus, this proof-of-concept study has shown ULM to be a promising imaging technique for qualitative and quantitative microvascular assessment. Imaging native kidneys in patients with kidney diseases will be needed to identify their ULM biomarkers.

Transfer Learning Based Deep Neural Network for Detecting Artefacts in Endoscopic Images

Endoscopy is typically used to visualize various parts of the digestive tract. The technique is well suited to detect abnormalities like cancer/polyp, taking sample tissue called a biopsy, or cauterizing a bleeding vessel. During the procedure, video/ images are generated. It is affected by eight different artefacts: saturation, specularity, blood, blur, bubbles, contrast, instrument and miscellaneous artefacts like floating debris, chromatic aberration etc. The frames affected by artefacts are mostly discarded as the clinician could extract no valuable information from them. It affects post-processing steps. Based on the transfer learning approach, three state-of-the-art deep learning models, namely YOLOv3, YOLOv4 and Faster R-CNN, were trained with images from EAD public datasets and a custom dataset of endoscopic images of Indian patients annotated for artefacts mentioned above. The training set of images are data augmented and used to train all the three-artefact detectors. The predictions of the artefact detectors are combined to form an ensemble model whose results outperformed well compared to existing literature works by obtaining a mAP score of 0.561 and an IoU score of 0.682. The inference time of 80.4ms was recorded, which stands out best in the literature.

Noise reduction in electrophysiological signals using transfer machine learning

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): NIH Background/Introduction Reducing electrophysiological signal noise is essential for diagnosis, mapping and ablation, yet most approaches are suboptimal. Template matching requires libraries of known signal types, that are difficult to obtain. Beat averaging can reduce noise, yet cannot be applied to single beats and obscures beat-to-beat variations. Beat smoothing can lose critical and subtle signal features. We set out to use neural networks (NN) based on encoder-decoders, which are able to extract key signal features and hence reconstruct them without noise and artifact. Purpose We hypothesised that electrograms with varying sources of artifact can be denoised using autoencoder neural networks. We further hypothesised that this could be achieved in a small data set by developing the method in a larger dataset of related signals, then using transfer learning. We tested this approach for atrial monophasic action potentials (MAPs) that have verifiable shapes. Methods The NN was first trained with 5706 left and right ventricular MAPs from 42 patients with ischemic cardiomyopathy (age 65±13y; fig 1.A): 60% for training, 20% (validation) and 20% (testing). Transfer learning and parameter-tuning were then used to apply this NN to a smaller sample of atrial MAPs (N=641, 21 patients, 67±5y, 13 women; fig D,F,H). Results The autoencoder was able to learn key features of MAPs, and hence reconstruct them without artifacts. NN learned ventricular MAPs with similarity coefficient 0.91±0.16, Pearson correlation 0.99± 0.01 (fig A) and learned key features (upstroke, triangular descent, terminus) to reduce noise (fig B-C). Applying this trained NN to atrial MAPs, the approach automatically eliminated ventricular artifact (fig E), high frequency noise (fig G), truncation (fig I), saturation and other artifacts. After fine-tuning, the NN reconstructed atrial MAPs with Pearson correlation = 0.99±0.01 (p&amp;lt;0.001). Conclusions Machine learned encoder-decoders are powerful tools that can automatically eliminate diverse types of noise in single beats by learning essential signal features. Transfer learning makes this possible without large datasets for training, even from signals in a different cardiac chamber. This approach may have far-reaching applications for mapping and ablation.

Endoscopy Artefact Detection by Deep Transfer Learning of Baseline Models.

To visualise the tumours inside the body on a screen, a long and thin tube is inserted with a light source and a camera at the tip to obtain video frames inside organs in endoscopy. However, multiple artefacts exist in these video frames that cause difficulty during the diagnosis of cancers. In this research, deep learning was applied to detect eight kinds of artefacts: specularity, bubbles, saturation, contrast, blood, instrument, blur, and imaging artefacts. Based on transfer learning with pre-trained parameters and fine-tuning, two state-of-the-art methods were applied for detection: faster region-based convolutional neural networks (Faster R-CNN) and EfficientDet. Experiments were implemented on the grand challenge dataset, Endoscopy Artefact Detection and Segmentation (EAD2020). To validate our approach in this study, we used phase I of 2,200 frames and phase II of 331 frames in the original training dataset with ground-truth annotations as training and testing dataset, respectively. Among the tested methods, EfficientDet-D2 achieves a score of 0.2008 (mAPd[Formula: see text]0.6+mIoUd[Formula: see text]0.4) on the dataset that is better than three other baselines: Faster-RCNN, YOLOv3, and RetinaNet, and competitive to the best non-baseline result scored 0.25123 on the leaderboard although our testing was on phase II of 331 frames instead of the original 200 testing frames. Without extra improvement techniques beyond basic neural networks such as test-time augmentation, we showed that a simple baseline could achieve state-of-the-art performance in detecting artefacts in endoscopy. In conclusion, we proposed the combination of EfficientDet-D2 with suitable data augmentation and pre-trained parameters during fine-tuning training to detect the artefacts in endoscopy.

Motion correction for three-dimensional chemical exchange saturation transfer imaging without direct water saturation artifacts.

In chemical exchange saturation transfer (CEST) MRI, motion correction is compromised by the drastically changing image contrast at different frequency offsets, particularly at the direct water saturation. In this study, a simple extension for conventional image registration algorithms is proposed, enabling robust and accurate motion correction of CEST-MRI data. The proposed method uses weighted averaging of motion parameters from a conventional rigid image registration to identify and mitigate erroneously misaligned images. Functionality of the proposed method was verified by ground truth datasets generated from 10 three-dimensional in vivo measurements at 3T with simulated realistic random rigid motion patterns and noise. Performance was assessed using two different criteria: the maximum image misalignment as a measure for the robustness against direct water saturation artifacts, and the spectral error as a measure of the overall accuracy. For both criteria, the proposed method achieved the best scores compared with two motion-correction algorithms specifically developed to handle the varying contrasts in CEST-MRI. Compared with a straightforward linear interpolation of the motion parameters at frequency offsets close to the direct water saturation, the proposed method offers better performance in the absence of artifacts. The proposed method for motion correction in CEST-MRI allows identification and mitigation of direct water saturation artifacts that occur with conventional image registration algorithms. The resulting improved robustness and accuracy enable reliable motion correction, which is particularly crucial for an automated and carefree evaluation of spectral CEST-MRI data, e.g., for large patient cohorts or in clinical routines.

High Dynamic Range Imaging for Dynamic Scenes With Large-Scale Motions and Severe Saturation

Two key challenges exist in high dynamic range (HDR) imaging from multiexposure low dynamic range (LDR) images for dynamic scenes: 1) aligning the input images with large-scale foreground motions and 2) recovering large saturated regions from a limited number of input LDR images. To tackle these challenges, several deep convolutional neural networks have been proposed that have made significant progress. However, these methods tend to suffer from ghosting and saturation artifacts when applied to some challenging scenes. In this article, we propose an end-to-end deformable HDR imaging network, called DHDRNet, which attempts to alleviate these problems by building an effective aligning module and adopting self-guided attention. First, we analyze the alignment process in the HDR imaging task and correspondingly design a pyramidal deformable module (PDM) that aligns LDR images at multiple scales and reconstructs aligned features in a coarse-to-fine manner. In this way, the proposed DHDRNet can handle large-scale complex motions and suppress ghosting artifacts caused by misalignments. Moreover, we adopt self-guided attention to reduce the influence of saturated regions during the aligning and merging processes, which helps suppress artifacts and retain fine details in the final HDR image. Extensive qualitative and quantitative comparisons demonstrate that the proposed model outperforms the existing start-of-the-art methods and that it is robust to challenging scenes with large-scale motions and severe saturation. The source code is available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Tx000/DHDRNet</uri> .

Attention-Guided Progressive Neural Texture Fusion for High Dynamic Range Image Restoration.

High Dynamic Range (HDR) imaging via multi-exposure fusion is an important task for most modern imaging platforms. In spite of recent developments in both hardware and algorithm innovations, challenges remain over content association ambiguities caused by saturation, motion, and various artifacts introduced during multi-exposure fusion such as ghosting, noise, and blur. In this work, we propose an Attention-guided Progressive Neural Texture Fusion (APNT-Fusion) HDR restoration model which aims to address these issues within one framework. An efficient two-stream structure is proposed which separately focuses on texture feature transfer over saturated regions and multi-exposure tonal and texture feature fusion. A neural feature transfer mechanism is proposed which establishes spatial correspondence between different exposures based on multi-scale VGG features in the masked saturated HDR domain for discriminative contextual clues over the ambiguous image areas. A progressive texture blending module is designed to blend the encoded two-stream features in a multi-scale and progressive manner. In addition, we introduce several novel attention mechanisms, i.e., the motion attention module detects and suppresses the content discrepancies among the reference images; the saturation attention module facilitates differentiating the misalignment caused by saturation from those caused by motion; and the scale attention module ensures texture blending consistency between different coder/decoder scales. We carry out comprehensive qualitative and quantitative evaluations and ablation studies, which validate that these novel modules work coherently under the same framework and outperform state-of-the-art methods.

Sequential Model for Digital Image Contrast Enhancement

Aims: The manuscript aims at designing and developing a model for optimum contrast enhancement of an input image. The output image of model ensures the minimum noise, the maximum brightness and the maximum entropy preservation. Objectives: * To determine an optimal value of threshold by using the concept of entropy maximization for segmentation of all types of low contrast images. * To minimize the problem of over enhancement by using a combination of weighted distribution and weighted constrained model before applying histogram equalization process. * To provide an optimum contrast enhancement with minimum noise and undesirable visual artefacts. * To preserve the maximum entropy during the contrast enhancement process and providing detailed information recorded in an image. * To provide the maximum mean brightness preservation with better PSNR and contrast. * To effectively retain the natural appearance of an images. * To avoid all unnatural changes that occur in Cumulative Density Function. * To minimize the problems such as noise, blurring and intensity saturation artefacts. Methods: 1. Histogram Building. 2. Segmentation using Shannon’s Entropy Maximization. 3. Weighted Normalized Constrained Model. 4. Histogram Equalization. 5. Adaptive Gamma Correction Process. 6. Homomorphic Filtering. Results: Experimental results obtained by applying the proposed technique MEWCHE-AGC on the dataset of low contrast images, prove that MEWCHE-AGC preserves the maximum brightness, yields the maximum entropy, high value of PSNR and high contrast. This technique is also effective in retaining the natural appearance of an images. The comparative analysis of MEWCHE-AGC with existing techniques of contrast enhancement is an evidence for its better performance in both qualitative as well as quantitative aspects. Conclusion: The technique MEWCHE-AGC is suitable for enhancement of digital images with varying contrasts. Thus useful for extracting the detailed and precise information from an input image. Thus becomes useful in identification of a desired regions in an image.

Inpainting for Saturation Artifacts in Optical Coherence Tomography Using Dictionary-Based Sparse Representation

Saturation artifacts in optical coherence tomography (OCT) occur when received signal exceeds the dynamic range of spectrometer. Saturation artifact shows a streaking pattern and could impact the quality of OCT images, leading to inaccurate medical diagnosis. In this paper, we automatically localize saturation artifacts and propose an artifact correction method via inpainting. We adopt a dictionary-based sparse representation scheme for inpainting. Experimental results demonstrate that, in both case of synthetic artifacts and real artifacts, our method outperforms interpolation method and Euler’s elastica method in both qualitative and quantitative results. The generic dictionary offers similar image quality when applied to tissue samples which are excluded from dictionary training. This method may have the potential to be widely used in a variety of OCT images for the localization and inpainting of the saturation artifacts.

3D Non-Rigid Alignment of Low-Dose Scans Allows to Correct for Saturation in Lower Extremity Cone-Beam CT.

Detector saturation in cone-beam computed tomography occurs when an object of highly varying shape and material composition is imaged using an automatic exposure control (AEC) system. When imaging a subject’s knees, high beam energy ensures the visibility of internal structures but leads to overexposure in less dense border regions. In this work, we propose to use an additional low-dose scan to correct the saturation artifacts of AEC scans. Overexposed pixels are identified in the projection images of the AEC scan using histogram-based thresholding. The saturation-free pixels from the AEC scan are combined with the skin border pixels of the low-dose scan prior to volumetric reconstruction. To compensate for patient motion between the two scans, a 3D non-rigid alignment of the projection images in a backward-forward-projection process based on fiducial marker positions is proposed. On numerical simulations, the projection combination improved the structural similarity index measure from 0.883 to 0.999. Further evaluations were performed on two in vivo subject knee acquisitions, one without and one with motion between the AEC and low-dose scans. Saturation-free reference images were acquired using a beam attenuator. The proposed method could qualitatively restore the information of peripheral tissue structures. Applying the 3D non-rigid alignment made it possible to use the projection images with inter-scan subject motion for projection image combination. The increase in radiation exposure due to the additional low-dose scan was found to be negligibly low. The presented methods allow simple but effective correction of saturation artifacts.

Fuzzy Mapped Histogram Equalization Method for Contrast Enhancement of Remotely Sensed Images

Histogram equalisation (HE) is a widely used image contrast enhancement technique which is less computationally complex, but it fails to preserve the brightness and natural appearance of the remotely sensed images. To overcome these limitations several modifications have been reported in the literature. However, the images processed by most of the methods still suffer with the problems of saturation artifacts and un-even expansion of intensities. This paper proposes a novel fuzzy mapped HE method to overcome the aforementioned limitations by partitioning the histogram into multiple segments, expanding each segment to full dynamic range using fuzzy mapping function, then equalising each segment independently, and finally normalising the combination of equalised segments. The normalisation process is controlled by a non-negative control factor, which requires a training data for its estimation. Experimental results demonstrate that the application of proposed method in the area of remote sensing yields better results than the contemporary methods.

Deep Tone Mapping Operator for High Dynamic Range Images.

A computationally fast tone mapping operator (TMO) that can quickly adapt to a wide spectrum of high dynamic range (HDR) content is quintessential for visualization on varied low dynamic range (LDR) output devices such as movie screens or standard displays. Existing TMOs can successfully tone-map only a limited number of HDR content and require an extensive parameter tuning to yield the best subjective-quality tone-mapped output. In this paper, we address this problem by proposing a fast, parameter-free and scene-adaptable deep tone mapping operator (DeepTMO) that yields a high-resolution and high-subjective quality tone mapped output. Based on conditional generative adversarial network (cGAN), DeepTMO not only learns to adapt to vast scenic-content (e.g., outdoor, indoor, human, structures, etc.) but also tackles the HDR related scene-specific challenges such as contrast and brightness, while preserving the fine-grained details. We explore 4 possible combinations of Generator-Discriminator architectural designs to specifically address some prominent issues in HDR related deep-learning frameworks like blurring, tiling patterns and saturation artifacts. By exploring different influences of scales, loss-functions and normalization layers under a cGAN setting, we conclude with adopting a multi-scale model for our task. To further leverage on the large-scale availability of unlabeled HDR data, we train our network by generating targets using an objective HDR quality metric, namely Tone Mapping Image Quality Index (TMQI). We demonstrate results both quantitatively and qualitatively, and showcase that our DeepTMO generates high-resolution, high-quality output images over a large spectrum of real-world scenes. Finally, we evaluate the perceived quality of our results by conducting a pair-wise subjective study which confirms the versatility of our method.

Benefit of Higher Maximum Force Output on Listening Effort in Bone-Anchored Hearing System Users: A Pupillometry Study

The aim of this study was to compare listening effort, as estimated via pupillary response, during a speech-in-noise test in bone-anchored hearing system (BAHS) users wearing three different sound processors. The three processors, Ponto Pro (PP), Ponto 3 (P3), and Ponto 3 SuperPower (P3SP), differ in terms of maximum force output (MFO) and MFO algorithm. The hypothesis was that listeners would allocate lower listening effort with the P3SP than with the PP, as a consequence of a higher MFO and, hence, fewer saturation artifacts in the signal. Pupil dilations were recorded in 21 BAHS users with a conductive or mixed hearing loss, during a speech-in-noise test performed at positive signal-to-noise ratios (SNRs), where the speech and noise levels were individually adjusted to lead to 95% correct intelligibility with the PP. The listeners had to listen to a sentence in noise, retain it for 3 seconds and then repeat it, while an eye-tracking camera recorded their pupil dilation. The three sound processors were tested in random order with a single-blinded experimental design. Two conditions were performed at the same SNR: Condition 1, where the speech level was designed to saturate the PP but not the P3SP, and condition 2, where the overall sound level was decreased relative to condition 1 to reduce saturation artifacts. The P3SP led to higher speech intelligibility than the PP in both conditions, while the performance with the P3 did not differ from the performance with the PP and the P3SP. Pupil dilations were analyzed in terms of both peak pupil dilation (PPD) and overall pupil dilation via growth curve analysis (GCA). In condition 1, a significantly lower PPD, indicating a decrease in listening effort, was obtained with the P3SP relative to the PP. The PPD obtained with the P3 did not differ from the PPD obtained with the other two sound processors. In condition 2, no difference in PPD was observed across the three processors. The GCA revealed that the overall pupil dilation was significantly lower, in both conditions, with both the P3SP and the P3 relative to the PP, and, in condition 1, also with the P3SP relative to the P3. The overall effort to process a moderate to loud speech signal was significantly reduced by using a sound processor with a higher MFO (P3SP and P3), as a consequence of fewer saturation artifacts. These findings suggest that sound processors with a higher MFO may help BAHS users in their everyday listening scenarios, in particular in noisy environments, by improving sound quality and, thus, decreasing the amount of cognitive resources utilized to process incoming speech sounds.

A Fusion of Cuckoo Search and Multiscale Adaptive Smoothing Based Unsharp Masking for Image Enhancement

Image enhancement means to improve the visual appearance of an image by increasing its contrast and sharpening the features. This article presents a fusion of cuckoo search optimization-based image enhancement (CS-IE) and multiscale adaptive smoothing based unsharping method (MAS-UM) for image enhancement. The fusion strategy is introduced to improve the deficiency of enhanced image that suppresses the saturation and over-sharpness artefacts in order to obtain a visually pleasing result. The ideology behind the selection of fusion images (candidate) is that one image should have high sharpness or contrast with maximum entropy and other should be high Peak Signal-to-Noise Ratio (PSNR) sharp image, to provide a better trade-off between sharpness and noise. In this article, the CS-IE and MAS-UM results are fused to combine their complementary advantages. The proposed algorithms are applied to lathe tool images and some natural standard images to verify their effectiveness. The results are compared with conventional enhancement techniques such as Histogram equalization (HE), Linear contrast stretching (LCS), Contrast-limited adaptive histogram equalization (CLAHE), standard PSO image enhancement (PSO-IE), Differential evolution image enhancement (DE-IE) and Firefly algorithm-based image enhancement (FA-IE) techniques.