Reduction Of Image Resolution Research Articles

Dual convolutional neural network for aberration pre-correction and image quality enhancement in integral imaging display.

This paper proposes a method that utilizes a dual neural network model to address the challenges posed by aberration in the integral imaging microlens array (MLA) and the degradation of 3D image quality. The approach involves a cascaded dual convolutional neural network (CNN) model designed to handle aberration pre-correction and image quality restoration tasks. By training these models end-to-end, the MLA aberration is corrected effectively and the image quality of integral imaging is enhanced. The feasibility of the proposed method is validated through simulations and optical experiments, using an optimized, high-quality pre-corrected element image array (EIA) as the image source for 3D display. The proposed method achieves high-quality integral imaging 3D display by alleviating the contradiction between MLA aberration and 3D image resolution reduction caused by system noise without introducing additional complexity to the display system.

Phase-assisted angular-multiplexing nanoprinting based on the Jacobi-Anger expansion.

Featuring with ultracompactness and subwavelength resolution, metasurface-assisted nanoprinting has been widely researched as an optical device for image display. It also provides a platform for information multiplexing, and a series of multiplexed works based on incident polarizations, operating wavelengths and observation angles have emerged. However, the angular-multiplexing nanoprinting is realized at the cost of image resolution reduction or the increase of fabrication difficulty, hindering its practical applications. Here, inspired by the Jacobi-Anger expansion, a phase-assisted design paradigm, called Bessel metasurface, was proposed for angular multiplexing nanoprinting. By elaborately designing the phase distribution of the Bessel metasurface, the target images can be encoded into the desired observation angles, reaching angular multiplexing. With the merits of ultracompactness and easy fabrication, we believe that our design strategy would be attractive in the real-world applications, including optical information storage, encryption/concealment, multifunctional switchable optical devices, and 3D stereoscopic displays, etc.

A novel image representation of GNSS correlation for deep learning multipath detection

This paper proposes a novel framework for multipath prediction in Global Navigation Satellite System (GNSS) signals. The method extends from dataset generation to deep learning inference through Convolutional Neural Network (CNN). The process starts at the output of the correlation stage of the GNSS receiver. Correlations of the received signal with a local replica over a (Doppler shift, propagation delay)-grid are mapped into gray scale 2D images. They depict the received information possibly contaminated by multipath propagation. The images feed a CNN for automatic feature construction and multipath pattern detection. The issue of unavailability of a large amount of supervised data required for CNN training has been overcome by the development of a synthetic data generator. It implements a well-established and documented theoretical model. A comparison of synthetic data with real samples is proposed. The complete framework is tested for various signal characteristics and algorithm parameters. The prediction accuracy does not fall below 93% for C/N0 ratio as low as 36 dBHz, corresponding to poor receiving conditions. In addition, the model turns out to be robust to the reduction of image resolution. Its performance is also measured and compared with an alternative Support Vector Machines (SVM) technique. The results show the undeniable superiority of the proposed CNN algorithm over the SVM benchmark.

Scrambling Based Riffle Shift on Stego-Image to Channelize the Ensured Data

In recent years information hiding has got much attention as it acts as an alternate option for secured communication. The Secret content would get imbedded with the image using various possible image embodiment techniques, in which the Least Significant Bit (LSB) substitution is one of the preferred content embodiment strategy; however, asserting the quality and the originality of the content embedded image (stego) is yet a grievous concern in the field of Information Security. In this article, a proficient Scrambling Based Haar Wavelet Transform (SBHWT) approach has been sought to ensure the novelty of the stego-image that supports the safe transfer of data over the channel. In this process, the image is scrambled using a Sudoku pattern, and then streams of the secret content are embedded into the image blocks based on the proposals of the Bishop Traversal logic of chess. The content embodiment is attempted using normal LSB substitution and DNA pattern based substitution. Next, the image is descrambled to recover the original image, which is then subjected to Haar wavelet transformation for image resolution reduction. The resultant reduced image is channelized to the recipient and the inverse action of the SBHWT is made in order to get back the secret code stream. The experimental results demonstrate that the projected SBHWT approach is less predictable and exclusive in its pattern.

Exploring Super-Resolution for Face Recognition

Biometric recognition is part of many aspects of modern society. With the popularization of smartphones, facial recognition gains space in this environment of biometric technologies. With the diversity of image capture devices, of different brands and qualities, the images will not always be in the ideal standard to be recognized. This article tests and compares different scenarios and situations to assess the results obtained by facial recognition in different environments. For this, the quantity method of data analysis was used. In the first scenario, all images were submitted without changes. In the following, we have the reduction of image resolution, which may or may not be followed by enlargement to the original resolution via bicubic interpolation or through the Image Super-Resolution algorithm, these images can be all, or only that undergo tests. Results indicate that the first scenario obtained the best performance, followed by only the tests images change. The worst performance occurs where the properties of all images are affected. In situations where there is a reduction and enlargement are optional, the enlargement option performs better, so the bicubic enlargement has an advantage over the ISR, the situation in which only the reduction occurs has the worst performance.

Автоматизоване розпізнавання міської рослинності та водойм за знімками супутника «Jilin-1А»

The results of testing the developed techniques for automatized recognition of vegetation and water bodies on the urban territory by multispectral images from the Jilin-1А satellite are given.The research included automatized recognition of vegetation and water bodies on the selected observation territory based on images with super high spatial resolution in the visual and infrared range and consequent comparison of the obtained results with the results of visual decoding. The obtained results of processing the images from the Jilin-1А satellite in accordance with the proposed techniques confirmed the sufficiently high accuracy of automatized edge enhancement of recognized objects as compared to the results of interactive visual recognition of these images. Different test areas provided a good separation of vegetation and water types with the same thresholding customization. The accuracy of automatized classification of vegetation and water bodies (without considering the standard errors) for different test areas was within 81...92%, and values of kappa-coefficient were within 0.68 to 0.85. Comparison of normalized index images received from Jilin-1А and Sentinel-2A satellites showed slight discordance in NDVI values and significant discordances for NDWI and MNDWI that are caused by the usage of different spectral channels (SWIR and NIR). These discordances can be sufficiently reduced when using correction coefficients.Analysis of the influence of output image resolution reduction (from 10 to 8 bit) and subsequent informational compressing (JPEG lossy and JPEG2000 lossless) on results of automatized recognition of vegetation and water bodies confirmed the validity and efficiency of these techniques. The volume of saved and transmitted files significantly decreased (in 80…100 times) with a slight reduction of classification accuracy (by 1...2 %). The proposed techniques make it possible to increase significantly the efficiency and probability of renewing maps of big cities and to reduce financial expenditures as compared to the traditional ground GPS-surveying and aerosurveying. The high-level automatization of image processing and minimization of necessary calculations (as compared to techniques that use complex classifiers and neural networks) allow to implement the developed technique as a geographic information web service that satisfies the needs of a wide circle of government services and commercial structures and can be useful for megalopolis population and tourists.

EDDs: A series of Efficient Defect Detectors for fabric quality inspection

Deep Convolutional Neural Network (DCNN) has recently advanced state-of-the-art performance on vision-related tasks and its application is further extended to industrial fields. The paper focuses on the problem of fabric defect detection to which an efficient DCNN architecture is developed. In contrast to previous methods that directly apply existing DCNN models demonstrated on natural images to industrial images, the proposed Efficient Defect Detectors (EDDs) are sufficiently optimized with consideration of the characteristics of fabric surface images, i.e., resolution, defect appearance, etc. Firstly, lightweight backbone is suggested in EDD to improve computational efficiency without reduction in image resolution. Secondly, a new feature fusion strategy named L-shaped feature pyramid network (L-FPN) is proposed and utilized to make full use of low-level texture features which are demonstrated to be more important than high-level semantic features in defect recognition. Based on the configurations of lightweight backbone and L-FPN, we use only one hyper-parameter to jointly adjust the proportion of resources occupied by width, depth and input resolution so that a family of defect detectors under different resource constraints can be developed. Experiments are conducted on a large fabric dataset to demonstrated the effectiveness of EDDs. Compared with the recent state-of-the-art detector, EfficientDet-d3, EDD-d3 achieves higher mean Average Precision (mAP) (20.9 vs 19.9) but with fewer parameters. EDD-d3 has 8.59M parameters and 31.78B FLOPs (floating point operation per second), which respectively are 39.8% and 49.0% lower than EfficientDet-d3. The proposed EDDs achieve better trade-off between accuracy and speed than previous methods. EDDs could be applied to fabric production sits with different resource restrictions, which demonstrates that EDDs have important application value.

Resolution Effect: An Error Correction Model for Intrinsic Permeability of Porous Media Estimated from Lattice Boltzmann Method

In digital rock physics, the intrinsic permeability of a porous rock sample can be evaluated from its micro-computed tomography ($$\upmu$$-CT) image through lattice Boltzmann method (LBM) simulation. The LBM permeability evaluation has been increasingly adopted by the oil and gas industries, especially when the access to core samples is limited. In order to accurately evaluate the permeability of porous media, this digital approach requires high-quality $$\upmu$$-CT images with sufficient resolution and size. In practice, however, the LBM simulation is often performed using images of reduced resolution, due to limitations in computing power and simulation time. As a result, the permeability results obtained are often compromised with significant errors, known as the resolution effect. In this study, the resolution effect is quantitatively investigated to identify the primary causes of error, based on which an error correction model for the LBM permeability evaluation is proposed. The model uses such geometric attributes as connected porosity, specific surface area and diffusion tortuosity to quantify the resolution effect and achieve error correction. Demonstrated on various types of porous media including sandstone, carbonate rock, sand pack, synthesis silica, etc., the proposed error correction model can effectively correct the errors in LBM permeability evaluation due to the resolution effect. Our error correction model makes image resolution reduction more meaningful and creditable for LBM permeability evaluation of porous media, thereby supporting its adoption in practical applications.

Influence of Drone Altitude, Image Overlap, and Optical Sensor Resolution on Multi-View Reconstruction of Forest Images

Recent technical advances in drones make them increasingly relevant and important tools for forest measurements. However, information on how to optimally set flight parameters and choose sensor resolution is lagging behind the technical developments. Our study aims to address this gap, exploring the effects of drone flight parameters (altitude, image overlap, and sensor resolution) on image reconstruction and successful 3D point extraction. This study was conducted using video footage obtained from flights at several altitudes, sampled for images at varying frequencies to obtain forward overlap ratios ranging between 91 and 99%. Artificial reduction of image resolution was used to simulate sensor resolutions between 0.3 and 8.3 Megapixels (Mpx). The resulting data matrix was analysed using commercial multi-view reconstruction (MVG) software to understand the effects of drone variables on (1) reconstruction detail and precision, (2) flight times of the drone, and (3) reconstruction times during data processing. The correlations between variables were statistically analysed with a multivariate generalised additive model (GAM), based on a tensor spline smoother to construct response surfaces. Flight time was linearly related to altitude, while processing time was mainly influenced by altitude and forward overlap, which in turn changed the number of images processed. Low flight altitudes yielded the highest reconstruction details and best precision, particularly in combination with high image overlaps. Interestingly, this effect was nonlinear and not directly related to increased sensor resolution at higher altitudes. We suggest that image geometry and high image frequency enable the MVG algorithm to identify more points on the silhouettes of tree crowns. Our results are some of the first estimates of reasonable value ranges for flight parameter selection for forestry applications.

Estimation of Fine-Scale Histologic Features at Low Magnification.

Whole-slide imaging has ushered in a new era of technology that has fostered the use of computational image analysis for diagnostic support and has begun to transfer the act of analyzing a slide to computer monitors. Due to the overwhelming amount of detail available in whole-slide images, analytic procedures-whether computational or visual-often operate at magnifications lower than the magnification at which the image was acquired. As a result, a corresponding reduction in image resolution occurs. It is unclear how much information is lost when magnification is reduced, and whether the rich color attributes of histologic slides can aid in reconstructing some of that information. To examine the correspondence between the color and spatial properties of whole-slide images to elucidate the impact of resolution reduction on the histologic attributes of the slide. We simulated image resolution reduction and modeled its effect on classification of the underlying histologic structure. By harnessing measured histologic features and the intrinsic spatial relationships between histologic structures, we developed a predictive model to estimate the histologic composition of tissue in a manner that exceeds the resolution of the image. Reduction in resolution resulted in a significant loss of the ability to accurately characterize histologic components at magnifications less than ×10. By utilizing pixel color, this ability was improved at all magnifications. Multiscale analysis of histologic images requires an adequate understanding of the limitations imposed by image resolution. Our findings suggest that some of these limitations may be overcome with computational modeling.

Low-light-level image super-resolution reconstruction based on iterative projection photon localization algorithm

The intensified charge-coupled device (ICCD) is widely used in the field of low-light-level (LLL) imaging. The LLL images captured by ICCD suffer from low spatial resolution and contrast, and the target details can hardly be recognized. Super-resolution (SR) reconstruction of LLL images captured by ICCDs is a challenging issue. The dispersion in the double-proximity-focused image intensifier is the main factor that leads to a reduction in image resolution and contrast. We divide the integration time into subintervals that are short enough to get photon images, so the overlapping effect and overstacking effect of dispersion can be eliminated. We propose an SR reconstruction algorithm based on iterative projection photon localization. In the iterative process, the photon image is sliced by projection planes, and photons are screened under the constraints of regularity. The accurate position information of the incident photons in the reconstructed SR image is obtained by the weighted centroids calculation. The experimental results show that the spatial resolution and contrast of our SR image are significantly improved.

EVALUATION OF MOVING OBJECT DETECTION BASED ON VARIOUS INPUT NOISE USING FIXED CAMERA

Abstract. Detecting and tracking objects in video has been as a research area of interest in the field of image processing and computer vision. This paper evaluates the performance of a novel method for object detection algorithm in video sequences. This process helps us to know the advantage of this method which is being used. The proposed framework compares the correct and wrong detection percentage of this algorithm. This method was evaluated with the collected data in the field of urban transport which include car and pedestrian in fixed camera situation. The results show that the accuracy of the algorithm will decreases because of image resolution reduction.

Monte Carlo based reduction of image resolution in view of no-reference image quality assessment

In the paper a fast algorithm of image downsampling is presented which is based on the estimation of mean local luminance using the Monte Carlo method assuming the division of an image into smaller non-overlapping blocks. Due to modifications proposed in the paper, one can obtain similar results than for classical methods compared using recently proposed no-reference image quality metrics. Nevertheless, further optimization requires the development of no-reference image quality metrics with higher accordance to human perception of typical distortions. Streszczenie. W artykule zaprezentowano szybki algorytm redukcji rozdzielczości obrazu oparty na estymacji średniej jasności w bloku z uzyciem metody Monte Carlo. Dzieki zaproponowanym modyfikacjom mozliwe jest uzyskanie rezultatow podobnych do klasycznych metod, porownywanych z wykorzystaniem metod ślepej oceny jakości obrazow opracowanych w ostatnich latach. Dalsza optymalizacja wymaga jednakze opracowania ślepych wskaźnikow jakości obrazu charakteryzujących sie wyzszą korelacją z percepcją typowych znieksztalcen obrazu przez czlowieka. Redukcja rozdzielczości obrazow bazująca na metodzie Monte Carlo w aspekcie ślepej oceny jakości obrazu

이미지 향상을 위해 공간영역에서 다중해상도를 이용한 개선된 히스토그램 특정화 방법

Usually, spatial information can be incorporated into histograms by taking histograms of a multiresolution image. For these reasons, many researchers are interested in multiresolution histogram processing. If the relation and sensitivity of the multiresolution images are well combined without loss of information, we can obtain satisfactory results in several fields of image processing including histogram equalization, specification and pattern matching. In this paper, we propose a multiresolution histogram specification method that improves the accuracy of histogram specification. The multiresolution decomposition technique is used in order to overcome the unique feature of a histogram specification affected by a quantization error of a digitalized image. The histogram specification is processed after the reduction of image resolution in order to enhance the accuracy of the results by histogram specification methods. The experimental results show that the proposed method enhances the accuracy of specification compared to conventional methods.

Evaluation of cranial window types forin vivotwo-photon imaging of brain microstructures

In vivo two-photon laser scanning microscopy (2PLSM) of neurons and glia in the mouse neocortex can be achieved using two distinct surgical methods: the thin-skull and open-skull window techniques. The open-skull window technique is more invasive, but provides more flexibility in imaging periods and frequencies. Critical evaluations of the two surgical methods are required, particularly their ability to image submicron structures such as dendritic filopodia and spines. Here, we used multiple approaches to evaluate the optical resolution of 2PLSM through two types of cranial windows. Two-photon point spread function (PSF) on the surface of the neocortex in vivo was studied by imaging the fluorescent microspheres attached to the pial surface. Comparable PSF widths indicated that the optical resolution of 2PLSM on the brain surface was similar between the two types of cranial window techniques. Next, we evaluated the effects of laser beam propagation through the thinned bone by imaging the underlying gel samples containing fluorescent microspheres as phantoms mimicking the brain tissue. We observed deterioration of PSF with increase in the imaging depths through the thinned bone. Reduction in image resolution through the thin-skull window in the deep cortical layer was confirmed by repetitive 2PLSM of the same GFP-positive dendritic spines with two techniques. In summary, the resolution of 2PLSM is comparable with those of two cranial window preparations in the superficial layer of the neocortex, but the quality of the image through the thin-skull windows degrades at points deeper than 50 µm.

Evaluation of an underwater solid state memory video system with application to fish abundance and diversity studies in southeast Australia

This paper describes a solid state memory (SSM) video camera system designed for extended deployment as a remote underwater video device with applications for measuring fish abundance and diversity. The video camera was evaluated in a series of lab-based trials to establish performance parameters (power consumption, data accumulation rates and maximum continuous recording time). The system performed reliably with a mean maximum continuous recording time of 13.3 (±0.3) h. Field trials were carried out to (a) compare results of video parameters, number of species identified (Nsp), maximum number of each species (MaxN) and time at first appearance of each species (TFAP) to a conventional standard definition video system and (b) examine the effect that a reduction in image resolution has on these parameters. Results indicated no significant differences between the standard definition camera and the test system at the highest resolution stetting (640 × 480). However, the total number of species identified by the reader was significantly less at the low resolution setting (176 × 144) and a significant reduction in relative abundance (MaxN) and TFAP were identified for both the mid (320 × 240) and lowest resolution (176 × 144) settings. Results indicate the solid state camera system is useful for extending recording time above that of standard definition cameras. However, there is limited potential to extend deployment times of BRUV systems via a reduction in video resolution without significant compromises to the accuracy and precision of the key indicators for diversity and abundance of fishes.

Comparison of Visually Coupled System Performance in Helicopter Flight and Simulator Trials

Astudy wasconductedtocompareandvalidatetwovisually coupledsystem (VCS)installations,oneinamovingbasee ightsimulatorandasecondinaBell205researchhelicopter.Standardlow-levelmaneuveringtaskswereused to examinechanges in handling qualities. Pilots also assessed two levels of control augmentation: rate damped and translational rate command. The system handling qualities degraded whenever the VCS was used. Pilots reported that there were system dee ciencies which increased their workload and prevented them from achieving desired task performance. The decline in handling qualities was attributed principally to the reduction in image quality while e ying the helicopter with theVCS. The primary factors affecting performance included a reduction in image resolution, a reduction in the e eld of view, system latencies, and the limitations of the simulator mathematical model. Control augmentation improved the system handling qualities in the simulator and should be investigated furtherasaneffectivestrategyforovercomingVCSlimitations.Thetrendsfoundinthesimulatorwerecomparable to those found in the helicopter and could be used to develop a VCS for military search and rescue applications.

Document skew estimation without angle range restriction

The existing skew estimation techniques usually assume that the input image is of high resolution and that the detectable angle range is limited. We present a more generic solution for this task that overcomes these restrictions. Our method is based on determination of the first eigenvector of the data covariance matrix. The solution comprises image resolution reduction, connected component analysis, component classification using a fuzzy approach, and skew estimation. Experiments on a large set of various document images and performance comparison with two Hough transform-based methods show a good accuracy and robustness for our method.

Improved Resolution and Signal-to-Noise Ratio in MRI via Enhanced Signal Digitization

The high frequencyk-space data in magnetic resonance imaging is often poorly reproduced due to the finite dynamic range of an analog-to-digital converter. The magnitude of this digitization error can equal and even exceed the magnitude of the thermal noise. Under such conditions, attempts to increase image signal-to-noise ratio via signal averaging meet with diminishing success. Because the relative size of the digitization error increases at higher spatial frequencies, a reduction in image resolution is incurred as well. By adjusting the level of the analog signal sampled by the analog-to-digital converter during the course of an imaging experiment, the magnitude of the digitization artifact can be greatly reduced. The results of simulations and imaging experiments are presented which demonstrate that this strategy improves both the signal-to-noise ratio and resolution of magnetic resonance images.

Film-screen systems: Sensitometric comparison of Kodak Ektavision system to Kodak T-Mat/RA system

Background. In 1995, Eastman Kodak Company (Rochester, N.Y.) marketed the Ektavision Extraoral Imaging Film and Screen System. It produced high-resolution images by keeping the light emitted by intensifying screens from “punching through” one emulsion layer on the film to another. Objectives. The objectives of the experiment were (1) to compare the sensitometric properties of the Ektavision system to its predecessor, the T-Mat/RA system and (2) to determine whether films and screens of the Ektavision and the T-Mat/RA systems could be interchanged without clinically changing the sensitometric properties. Ektavision and Lanex Regular intensifying screens were used in combination with Ektavision, T-Mat G/RA, T-Mat L/RA, and T-Mat H/RA films. Results. The results showed that the Ektavision film was slightly slower than the T-Mat G/RA film no matter what type of screen was used. The type of screen used had little effect on inherent contrast or exposure latitude. The Ektavision film had approximately similar inherent contrast as the T-Mat G/RA film but less than that of the T-Mat H/RA film; its latitude was less than that of the T-Mat L/RA film. The manufacturer does not recommend combining T-Mat/RA film with Ektavision screens or Lanex Regular screens with Ektavision film because of a possible reduction in image resolution. But this mismatch did not clinically affect inherent contrast and exposure latitude; however, film speed was slightly affected. Conclusions. The Ektavision system is the result of advanced technology that produces images of high resolution. The high-resolution Ektavision film is slightly slower than the T-Mat G/RA film but is equivalent to it in its contrast and latitude. Therefore when changing from the combination of T-Mat/RA film plus Lanex Regular screens to that of Ektavision film plus Ektavision screens, a slight increase in x-radiation exposure should be made.