Information Content Of Images Research Articles

Saccadic "inhibition" unveils the late influence of image content on oculomotor programming.

Image content is prioritized in the visual system. Faces are a paradigmatic example, receiving preferential processing along the visual pathway compared to other visual stimuli. Moreover, face prioritization manifests also in behavior. People tend to look at faces more frequently and for longer periods, and saccadic reaction times can be faster when targeting a face as opposed to a phase-scrambled control. However, it is currently not clear at which stage image content affects oculomotor planning and execution. It can be hypothesized that image content directly influences oculomotor signal generation. Alternatively, the image content could exert its influence on oculomotor planning and execution at a later stage, after the image has been processed. Here we aim to disentangle these two alternative hypotheses by measuring the frequency of saccades toward a visual target when the latter is followed by a visual transient in the central visual field. Behaviorally, this paradigm leads to a reduction in saccade frequency that happens about 90 ms after any visual transient event, also known as saccadic "inhibition". In two experiments, we measured occurrence of saccades in visually guided saccades as well as microsaccades during fixation, using face and noise-matched visual stimuli. We observed that while the reduction in saccade occurrence was similar for both stimulus types, face stimuli lead to a prolonged reduction in eye movements. Moreover, saccade kinematics were altered by both stimulus types, showing an amplitude reduction without change in peak velocity for the earliest saccades. Taken together, our experiments imply that face stimuli primarily affect the later stages of the behavioral phenomenon of saccadic "inhibition". We propose that while some stimulus features are processed at an early stage and can quickly influence eye movements, a delayed signal conveying image content information is necessary to further inhibit/delay activity in the oculomotor system to trigger eye movements.

Phase-diversity-based wavefront sensing for fluorescence microscopy

Fluorescence microscopy is an invaluable tool in biology, yet its performance is compromised when the wavefront of light is distorted due to optical imperfections or the refractile nature of the sample. Such optical aberrations can dramatically lower the information content of images by degrading the image contrast, resolution, and signal. Adaptive optics (AO) methods can sense and subsequently cancel the aberrated wavefront, but they are too complex, inefficient, slow, or expensive for routine adoption by most labs. Here, we introduce a rapid, sensitive, and robust wavefront sensing scheme based on phase diversity, a method successfully deployed in astronomy but underused in microscopy. Our method enables accurate wavefront sensing to less than λ/35 root mean square (RMS) error with few measurements, and AO with no additional hardware besides a corrective element. After validating the method with simulations, we demonstrate the calibration of a deformable mirror >hundredfold faster than comparable methods (corresponding to wavefront sensing on the ∼100ms scale), and sensing and subsequent correction of severe aberrations (RMS wavefront distortion exceeding λ/2), restoring diffraction-limited imaging on extended biological samples.

Cosine-enhanced tuna swarm optimized exponential entropy segmentation method for sand grain microscopic images

The feature extraction of sand grain size, color and texture is a necessary step to identify clastic components. The sand grain features are complex and various, which brings difficulties to geological identification. Aiming at these images, a cosine-enhanced tuna swarm optimized exponential entropy segmentation method is proposed, which can effectively preserve the texture features of various sand grains. Firstly, for the tuna swarm optimization (TSO) algorithm, three improvement strategies are proposed: cosine spiral movement, cosine parabolic movement and Gauss-Cauchy mutation, which improve the TSO's global and local search. This algorithm is called cosine-enhanced TSO (CETSO). Benchmark function experiments showed that the convergence accuracy and stability of CETSO are greatly improved, and the convergence speed is also slightly increased. Secondly, CETSO optimized the exponential entropy to automatically determine the segmentation thresholds, and the feasibility of the method is verified by taking the information content of the segmented image as the standard. Finally, segmentation experiments were carried out on the Yarlung Zangbo River sand microscopic image dataset, and the results show that the method has high segmentation accuracy and stability for images with high contrast, rich texture, or significant differences in the size of sand debris. Compared with TSO, the CETSO optimized exponential entropy segmented images achieved an improvement of 21% and 93% in the evaluation of the average and standard deviation of the peak signal-to-noise ratio on thirty experiments. And this method has a fast processing speed, and it only takes about 0.85s to divide an image on average, which meets the needs of engineering applications.

Agreement in the Postural Assessment of Older Adults by Physical Therapists Using Clinical and Imaging Methods.

Postural assessment is one of the indicators of health status in older adults. Since the number of older adults is on the rise, it is essential to assess simpler methods and automated ones in the future. Therefore, we focused on a visual method (imaging method). The purpose of this study is to determine the degree of agreement between the imaging method and the palpation and visual methods (clinical method). In addition, the influence of differences in the information content of the sagittal plane images on the assessment was also investigated. In this experiment, 28 sagittal photographs of older adults whose posture had already been assessed using the clinical method were used. Furthermore, based on these photographs, 28 gray and silhouette images (G and S images) were generated, respectively. The G and S images were assessed by 28 physical therapists (PTs) using the imaging method. The assessment was based on the Kendall classification, with one of four categories selected for each image: ideal, kyphosis lordosis, sway back, and flat back. Cross-tabulation matrices of the assessments using the clinical method and imaging method were created. In this table, four categories and two categories of ideal and non-ideal (KL, SB, and FB) were created. The agreement was evaluated using the prevalence-adjusted bias-adjusted kappa (PABAK). In addition, sensitivity and specificity were calculated to confirm the reliability. When comparing the clinical and imaging methods in the four posture categories, the PABAK values were -0.14 and -0.29 for the S and G images, respectively. In the case of the two categories, the PABAK values were 0.57 and 0.5 for the S and G images, respectively. The sensitivity and specificity were 86% and 57% for the S images and 76% and 71% for the G images, respectively. The four categories show that the imaging method is difficult to assess regardless of the image processing. However, in the case of the two categories, the same assessment of the clinical method applied to the imaging method for both the S and G images. Therefore, no differences in image processing were observed, suggesting that PTs can identify posture using the visual method.

Physical model-based ArF photoresist formulation development

Since the logic 28 nm technology and beyond, ArF immersion lithography has been widely used in manufacturing. To fully utilize the potential of the lithographic resolution, process simulation has been used since the lithography process setup step. The accuracy of the model prediction can be a key factor in the process stability, defectivity, and yield of the final product. To get better model prediction, it is very important to develop a process model with as many parameters as possible with physical meanings. Since the analysis of exposure data with a physical model can provide insights into the process and the photoresist material, it may greatly accelerate the photoresist formulation development or improvement process. From another aspect, as the function of the photoresist is to record the aerial image information on wafer, theoretically, the photoresist image should not deviate much from the aerial image or it will inevitably reduce the information content of the aerial image, i.e., through various kinds of averaging, mixing, etc. Since the emergence of Optical Proximity Correction (OPC), the time that takes to finalize a photolithography process has been significantly lengthened and made more complicated. To make the OPC model more reliable and less dependent on patches, a more physical OPC model is necessary. Therefore, it is clear that if we can develop a photoresist formulation to realize a more physical photoresist image, or a photoresist image closer to the aerial image, we can greatly improve the photolithography process performance and the time consumed for the process setup including OPC. In this paper, we focus on several physical model parameters for photoresist, especially the effective photoacid diffusion length and photo decomposable base parameters. We will show that these two parameters can provide a good description of photoresist imaging behavior, and the resist formulation can be improved to match the physical model prediction with more accuracy.

Multi-View Data-Based Layover Information Compensation Method for SAR Image Mosaic

Currently, massive Synthetic Aperture Radar (SAR) images acquired from numerous SAR satellites have been widely utilized in various fields, and image mosaicking technology provides important support and assistance for these applications. The traditional mosaic method selects specific SAR images that can cover the region of interest (ROI) from redundant data to produce “One Map”. However, an SAR image suffers from severe geometric distortion, especially in mountainous areas, which inevitably reduces the utilization of mosaic image. Therefore, a multi-view data-based layover information compensation (MDLIC) method for SAR image mosaic is proposed, aiming to take full advantage of multi-view data to compensate for the missing information in the layover area of the SAR image. This is performed to improve the information content of the mosaic image and realize efficient thematic information extraction and analysis. First, the calculation of the object-space extent of all images and the division of object-space grid are completed on the basis of geometric and radiometric preprocessing. Then, according to the transformation relationship between the object-space and the image-space, the sampling rate image of each image corresponding to the object-space grid is generated, which determines the layover area and the layover degree in each image. Finally, the information compensation strategy is implemented in accordance with the sampling rate image to realize the compensation of the layover information. The feasibility and effectiveness of the MDLIC method are verified by using multiple SAR images from the Chinese Gaofen-3 01 satellite as datasets for experiments. The experimental results indicate that the MDLIC method can obtain mosaic images with richer information compared with the traditional method, while still providing satisfactory results.

Image restoration of degraded time-lapse microscopy data mediated by near-infrared imaging.

Time-lapse fluorescence microscopy is key to unraveling biological development and function; however, living systems, by their nature, permit only limited interrogation and contain untapped information that can only be captured by more invasive methods. Deep-tissue live imaging presents a particular challenge owing to the spectral range of live-cell imaging probes/fluorescent proteins, which offer only modest optical penetration into scattering tissues. Herein, we employ convolutional neural networks to augment live-imaging data with deep-tissue images taken on fixed samples. We demonstrate that convolutional neural networks may be used to restore deep-tissue contrast in GFP-based time-lapse imaging using paired final-state datasets acquired using near-infrared dyes, an approach termed InfraRed-mediated Image Restoration (IR2). Notably, the networks are remarkably robust over a wide range of developmental times. We employ IR2 to enhance the information content of green fluorescent protein time-lapse images of zebrafish and Drosophila embryo/larval development and demonstrate its quantitative potential in increasing the fidelity of cell tracking/lineaging in developing pescoids. Thus, IR2 is poised to extend live imaging to depths otherwise inaccessible.

О ПРИМЕНЕНИИ СПЕКТРОЗОНАЛЬНОЙ ВИЗУАЛИЗАЦИИ В ДИСТАНЦИОННОМ ЗОНДИРОВАНИИ ЗЕМЛИ

The issues of using multispectral imaging in remote sensing of the Earth, both using satellite images and using unmanned aerial vehicles (UAVs), are discussed. The use of multispectral images for assessing reforestation, the effectiveness of herbicide use, reconstruction of archaeological sites, environmental monitoring of the surface of reservoirs by monitoring the extent of the spread of blue-green algae, and recognition of the road network in forestry is described. The feasibility of using UAVs to survey individual areas of the terrain instead of obtaining images from satellites is assessed. The results of increasing the information content of multispectral images obtained using various processing options are presented. Based on the results of the analytical review, trends in the development of methods for improving the visual quality of multispectral images obtained during monitoring of the earth's surface are formulated. The disadvantages of the considered methods for increasing the information content of multispectral images are indicated, and improvements are proposed that can improve the methods used.

Learning Many-to-Many Mapping for Unpaired Real-World Image Super-resolution and Downscaling.

Learning based single image super-resolution (SISR) for real-world images has been an active research topic yet a challenging task, due to the lack of paired low-resolution (LR) and high-resolution (HR) training images. Most of the existing unsupervised real-world SISR methods adopt a twostage training strategy by synthesizing realistic LR images from their HR counterparts first, then training the super-resolution (SR) models in a supervised manner. However, the training of image degradation and SR models in this strategy are separate, ignoring the inherent mutual dependency between downscaling and its inverse upscaling process. Additionally, the ill-posed nature of image degradation is not fully considered. In this paper, we propose an image downscaling and SR model dubbed as SDFlow, which simultaneously learns a bidirectional manyto- many mapping between real-world LR and HR images unsupervisedly. The main idea of SDFlow is to decouple image content and degradation information in the latent space, where content information distribution of LR and HR images is matched in a common latent space. Degradation information of the LR images and the high-frequency information of the HR images are fitted to an easy-to-sample conditional distribution. Experimental results on real-world image SR datasets indicate that SDFlow can generate diverse realistic LR and SR images both quantitatively and qualitatively.

Blind Image Quality Assessment via Deep Response Feature Decomposition and Aggregation

Image quality is related to image content and distortion information. Most learning-based image quality assessment (IQA) methods extract quality-oriented features with auxiliary tasks like detecting the distortion type and level. However, the perceptual quality degradation caused by the same distortion type and level varies substantially for different content in an image. To deal with this problem, in this paper, we propose a blind IQA method based on Deep Response fEAture decoMposition and aggregation (DREAM), which considers two factors affecting the image quality simultaneously. First, we use a convolutional neural network (CNN) to extract the basic features from the input image. Second, several parallel fully connected (FC) layers are employed to decompose these basic features into response features related to the image content, distortion type, and distortion level. Third, the graph attention network (GAT) is leveraged to aggregate these response features corresponding to the visual quality. Finally, a regression network is used to predict the quality score. The success of our method lies in the feature decomposition to obtain the response features of different content to a specific distortion in the given distorted image and the quality-oriented features obtained by feature aggregation using the internal relation of these response features. Experimental results indicate that our proposed DREAM achieves state-of-the-art (SOTA) performance on both synthetic and authentic distortion IQA datasets.

Long-Tailed Object Detection for Multimodal Remote Sensing Images

With the rapid development of remote sensing technology, the application of convolutional neural networks in remote sensing object detection has become very widespread, and some multimodal feature fusion networks have also been proposed in recent years. However, these methods generally do not consider the long-tailed problem that is widely present in remote sensing images, which limits the further improvement of model detection performance. To solve this problem, we propose a novel long-tailed object detection method for multimodal remote sensing images, which can effectively fuse the complementary information of visible light and infrared images and adapt to the imbalance between positive and negative samples of different categories. Firstly, the dynamic feature fusion module (DFF) based on image entropy can dynamically adjust the fusion coefficient according to the information content of different source images, retaining more key feature information for subsequent object detection. Secondly, the instance-balanced mosaic (IBM) data augmentation method balances instance sampling during data augmentation, providing more sample features for the model and alleviating the negative impact of data distribution imbalance. Finally, class-balanced BCE loss (CBB) can not only consider the learning difficulty of specific instances but also balances the learning difficulty between categories, thereby improving the model’s detection accuracy for tail instances. Experimental results on three public benchmark datasets show that our proposed method achieves state-of-the-art performance; in particular, the optimization of the long-tailed problem enables the model to meet various application scenarios of remote sensing image detection.

Non-Local SAR Image Despeckling Based on Sparse Representation

Speckle noise is an inherent problem of synthetic aperture radar (SAR) images, which not only seriously affects the acquisition of SAR image information, but also greatly reduces the efficiency of image segmentation and feature classification. Therefore, research on how to effectively suppress speckle noise while preserving SAR image content information as much as possible has received increasing attention. Based on the non-local idea of SAR image block-matching three-dimensional (SAR-BM3D) algorithm and the concept of sparse representation, a novel SAR image despeckling algorithm is proposed. The new algorithm uses K-means singular value decomposition (K-SVD) to learn the dictionary to distinguish valid information and speckle noise and constructs a block filter based on K-SVD for despeckling, so as to avoid strong point diffusion problem in SAR-BM3D and achieve better speckle noise suppression with stronger adaptability. The experimental results on real SAR images show that the proposed algorithm achieves better comprehensive effect of speckle noise suppression in terms of evaluation indicators and information preservation of SAR images compared with several existing algorithms.

DEANet: Decomposition Enhancement and Adjustment Network for Low-Light Image Enhancement

Poor illumination greatly affects the quality of obtained images. In this paper, a novel convolutional neural network named DEANet is proposed on the basis of Retinex for low-light image enhancement. DEANet combines the frequency and content information of images and is divided into three subnetworks: decomposition, enhancement, and adjustment networks, which perform image decomposition; denoising, contrast enhancement, and detail preservation; and image adjustment and generation, respectively. The model is trained on the public LOL dataset, and the experimental results show that it outperforms the existing state-of-the-art methods regarding visual effects and image quality.

Evaluating the use of Instagram images color histograms and hashtags sets for automatic image annotation.

Color similarity has been a key feature for content-based image retrieval by contemporary search engines, such as Google. In this study, we compare the visual content information of images, obtained through color histograms, with their corresponding hashtag sets in the case of Instagram posts. In previous studies, we had concluded that less than 25% of Instagram hashtags are related to the actual visual content of the image they accompany. Thus, the use of Instagram images' corresponding hashtags for automatic image annotation is questionable. In this study, we are answering this question through the computational comparison of images' low-level characteristics with the semantic and syntactic information of their corresponding hashtags. The main conclusion of our study on 26 different subjects (concepts) is that color histograms and filtered hashtag sets, although related, should be better seen as a complementary source for image retrieval and automatic image annotation.

Direct Measurement and Imaging of Redox Status with Electron Paramagnetic Resonance.

Significance: Fundamental to the application of tissue redox status to human health is the quantification and localization of tissue redox abnormalities and oxidative stress and their correlation with the severity and local extent of disease to inform therapy. The centrality of the low-molecular-weight thiol, glutathione, in physiological redox balance has long been appreciated, but direct measurement of tissue thiol status in vivo has not been possible hitherto. Recent advances in instrumentation and molecular probes suggest the feasibility of real-time redox assessment in humans. Recent Advances: Recent studies have demonstrated the feasibility of using low-frequency electron paramagnetic resonance (EPR) techniques for quantitative imaging of redox status in mammalian tissues in vivo. Rapid-scan (RS) EPR spectroscopy and imaging, new disulfide-dinitroxide spin probes, and novel analytic techniques have led to significant advances in direct, quantitative imaging of thiol redox status. Critical Issues: While novel RS EPR imaging coupled with first-generation molecular probes has demonstrated the feasibility of imaging thiol redox status in vivo, further technical advancements are desirable and ongoing. These include developing spin probes that are tailored for specific tissues with response kinetics tuned to the physiological environment. Equally critical are RS instrumentation with higher signal-to-noise ratio and minimal signal distortion, as well as optimized imaging protocols for image acquisition with sparsity adapted to image information content. Future Directions: Quantitative images of tissue glutathione promise to enable acquisition of a general image of mammalian and potentially human tissue health.

Three level automatic segmentation of optic disc using LAB color space contours and morphological operation

AbstractFundus disorders like Glaucoma are a serious health risk that affects people all over the world, lowering their quality of life. Optic disc (OD) segmentation is a crucial step for automatic detection of glaucoma with fundus images. This paper presents an efficient and accurate OD segmentation methodology from eye fundus images. In the proposed methodology, fundus images are transformed from RGB (Red, Green, and Blue) color space to LAB (Luminance, A‐axis, B‐axis) color space. A new image is generated using the luminance, A‐axis component, and image information content. As a pre‐segmentation stage, the unsupervised approach extracts the region of interest containing OD. The adaptive threshold method, followed by morphological operations, extracts the contours of the OD region and removes the spur. Segmentation of OD is a challenging task due to similar intensity levels with neighborhood, vascular occlusion, and retinal atrophy. The proposed work overcomes the challenges by segmentation of the OD at three levels, which enhances the functionality at different illumination conditions. The features are extracted at various levels to determine whether the segmented image contains the OD or not. The efficiency of the proposed methodology demonstrates by applying it to a variety of fundus images from datasets, namely ISBI IDRiD, DRISHTI‐GS, and SYSU. Key performance metrics like Intersection over Union and Dice Coefficient are used to evaluate the performance of the proposed methodology. The proposed methodology achieves Intersection over Union of 0.996, 0.997, and 0.994 on ISBI IDRiD, DRISHTI‐GS, and SYSU datasets, respectively. Similarly, the proposed methodology obtains 0.96, 0.97, and 0.98 Dice Coefficient on the three datasets.

A Large Scale Analysis for Testing a Mathematical Model for the Study of Vascular Pathologies

In this paper, we carry out a study developed on 13,677 images from 15 patients affected by moderate/severe atheromatous disease of the abdominal aortic tract. A procedure to extract the pervious lumen of the aorta artery from basal CT images is exploited and tested on a large scale. In particular, the above method takes advantage of the reconstruction and enhancing properties of the sampling Kantorovich algorithm which allows the information content of images to be increased. The processed image is compared, slice by slice, by superposition, with the corresponding contrast medium reference image. Numerical indices of errors were computed and analyzed in order to test the validity of the proposed method. The results achieved confirm, both from the numerical and clinical point of view, the good performance and accuracy of the proposed method, opening the possibility to perform an assisted diagnosis avoiding the injection of the contrast medium.

Image Style Transfer based on DeepLabV3+ Semantic Segmentation

Image style transfer has always been a popular research method in the computer vision community, which aims to learn the style of a given image and the content distribution of other images to generate new images with both the above style and content. Thanks to the rapid development of convolutional neural networks, the accuracy, and visualization of image segmentation and migration have been continuously improved, but there is still the problem of local content distortion. Some recent works introduce the segmentation branch to obtain pixel-level content information in order to obtain a more perfect transfer effect. In this paper, we use the VGG19 convolutional neural network model to extract high-level feature maps representing image content information and perform image style transfer based on the DeepLabV3+ semantic segmentation network, which can move images to the same or similar semantic regions during the entire transmission process. In order to prevent content distortion after image migration, we also introduce an affine function to control the content change of the image during image migration. Extensive experimental results show that the method in this paper can further clarify the segmentation boundary and improve the semantic accuracy of the transferred image. In addition, we also evaluated the expectations of different individuals on the degree of style transfer. By distributing the survey, this paper demonstrates that the method described in this study results in better image quality, more in line with the expectations of the majority of respondents.

Occluded Visible-Infrared Person Re-Identification

Visible-infrared person re-identification (VI-ReID) aims to match person images between the visible and near-infrared modalities. Previous VI-ReID methods are based on holistic pedestrian images and achieve excellent performance. However, in real-world scenarios, images captured by visible and near-infrared cameras usually contain occlusions. The performance of these methods degrades significantly due to the loss of information of discriminative features from the occlusion of the images. We define visible-infrared person re-identification in this occlusion scene as Occluded VI-ReID, where only partial content information of pedestrian images can be used to match images of different modalities from different cameras. In this paper, we propose a matching framework for occlusion scenes, which contains a local feature enhance module (LFEM) and a modality information fusion module (MIFM). LFEM adopts Transformer to learn features of each modality, and adjusts the importance of patches to enhance the representation ability of local features of the non-occluded areas. MIFM utilizes a co-attention mechanism to infer the correlation between each image for reducing the difference between modalities. We construct two occluded VI-ReID datasets, namely Occluded-SYSU-MM01 and Occluded-RegDB datasets. Our approach outperforms existing state-of-the-art methods on two occlusion datasets, while remains top performance on two holistic datasets.

MLNet: A Multi-Domain Lightweight Network for Multi-Focus Image Fusion

Existing multi-focus image fusion (MFIF) methods are difficult to achieve satisfactory results in both fusion performance and rate simultaneously. The spatial domain methods are hard to determine the focus/defocus boundary (FDB), and the transform domain methods are likely to damage the content information of the source images. Moreover, the deep learning-based MFIF methods are usually confronted with low rate due to complex models and enormous learnable parameters. To address these issues, we propose a multi-domain lightweight network (MLNet) for MFIF, which can achieve competitive results in both performance and rate. The proposed MLNet mainly includes three modules, namely focus extraction (FE), focus measure (FM) and image fusion (IF). In the interpretable FE module, the image features extracted by discrete cosine transform-based convolution (DCTConv) and local binary pattern-based convolution (LBPConv) are concatenated and fed into the FM module. DCTConv based on transform domain takes DCT coefficients to construct a fixed convolution kernel without parameter learning, which can effectively capture the high/low frequency content of the image. LBPConv based on spatial domain can achieve structure features and gradient information from source images. In the FM module, a 3-layer 1×1 convolution with a few learnable parameters is employed to generate the initial decision map, which has the properties of flexible input. The fused image is obtained by the IF module according to the final decision map. In terms of quantitative and qualitative evaluations, extensive experiments validate that the proposed method outperforms existing state-of-the-art methods on three public datasets. In addition, the proposed MLNet contains only 0.01M parameters, which is 0.2% of the first CNN-based MFIF method [25].