Super high resolution for long-range imaging

In the actual optical imaging system, in order to increase cell sensitivity or meet the needs of a large field of view, a large size CCD pixel is usually chosen as detection unit. This can not meet the Nyquist sampling theorem, and it belongs to ill-sampling. The geometric resolution of optical diffraction limited system image is directly restricted by the size of CCD pixel. In this paper, a reasonable optical mask is chosen, and this ensures the access to the CCD image information before sampling is lossless. Space spectral filtering technology is used, and appropriate resolution of the image is acquired. The method improves the resolution decline due to ill-sampling. By mathematical and simulation analysis, the effect of geometric super resolution image can be achieved.

Multiplexed Superresolution CRISPR Imaging of Chromatin in Living Cells

<p>Pneumonia is a severe lung infection, chest X-ray (CXR) image preferred to find infection. Real images lost its quality, resolution and other feature due to transmission. So good qualitative datasets are very limited. Quality enhancement in medical images is challenging task for researchers. And quality in clinical diagnosis of any disease in deep learning play a very important role. So, this paper presents an aspect with importance of quality in medical images CXR of a particular dataset and how to enhance and create new images with high quality resolution, that is re-used for classification in deep learning. Super resolution convolutional neural netwok (SRCNN) is deep learning based method, which is used for improving resolution in image. Super resolution means low resolution (LR) images from dataset is to be reconstructed or magnified into high resolution (HR). The objective behind this study is to measure the effect of super resolution with quality index, peak signal-to-noise ratio (PSNR), mean squared error (MSE), and structural similarity index measure (SSIM). This experinment performed on 200 images with 10 batches, each batch has 20 images from Kermany dataset, select LR images and converted into HR with SRCNN. Then we find PSNR value of image is increase upto 2 to 5 DB, and MSE of qood quality images is near to zero and MSE decrease up to 20-25, SSIM value have little variation due to same pattern is found in input and output images. Enhancement means highlight or improve the region of interest of pneumonic images. Main goal of this study is to preapare a modified dataset which is further used for classification.</p>

Head-related transfer functions (HRTFs) describe the acoustical transmission process from a point sound source to two ears in the free field. They are vital to the researches of binaural hearing and virtual auditory display. Measurement is the most important approach to obtain HRTFs, but it is time-consuming. To accurate HRTF measurement, a computer-controlling measurement system is designed. The system consists of multiple sound sources at different elevations with a finest interval of 5° and a horizontal turntable with an azimuthal resolution of 0.1°. It is able to work in non-anechoic environment and suitable for both mannequin and human subject measurement. A practical example indicates that the system allows for measuring far-field HRTFs at 493 source directions within half an hour. By using the system, a super high directional resolution HRTF database for KEMAR mannequin is established. The database includes 3889 pairs of head-related impulse responses (HRIRs, the time domain counterpart of HRTFs) at distance of 1.0 m, elevation interval of 5° (from -45° to 90° ), and azimuthal interval of 2.5°. Each HRIR is 1024-point length at 96 kHz sampling frequency and 24-bit quantization. The database is applicable to the research on binaural hearing and virtual auditory display.

In traditional optical microscopy imaging system, the resolution of time mainly depends on the detector’s detection speed, usually in millisecond or microsecond magnitude. While the spatial resolution is limited by the optical diffraction limit, the lateral resolution of ordinary microscopies generally only reaches 200nm.Just as biological structure has a wide spatial scale, biological living processes also have a broad time scale. When observing biological subcellular organelles, resolution and the speed of life activities should be considered. For the dynamic process, it’s meaningless to simply improve the spatial resolution without correspondingly increasing the imaging speed which should be no less than the movement speed of the observed object. Existing super-resolution or high-speed optical imaging is limited by the mutual constraints of spatial and time resolution, making it difficult to obtain both super-resolution and high-speed optical imaging. In order to break the constraints of this game and gain high-speed super-resolution images, the CS-SIM system combines SIM (structured illumination microscopy) and CSP (compressed sensing photography). Since they both are wide filed imaging and CSP is a passive receiving imaging technology, SIM and CSP have potential to combine closely and achieve super-resolution and highspeed imaging.

This study aimed to compare ocular wavefront aberrations for pupil diameters of 4 mm and 6 mm, and contrast sensitivity, in eyes with AcrySof IQ and AcrySof Natural intraocular lenses (IOLs). Sixty eyes of 60 patients were enrolled in this prospective randomized study. After phacoemulsification the eyes received either AcrySof IQ SN60WF or AcrySof Natural SN60AT IOLs. One month after surgery, all patients underwent complete ophthalmological examination including corneal topography, wavefront analysis for pupil diameters of 4 mm and 6 mm, and contrast sensitivity measurements with the CSV 1000E instrument under photopic and mesopic conditions with and without glare. There was no statistically significant difference between groups in age, sex or other preoperative ocular characteristics (p > 0.05). Patients with AcrySof IQ IOLs had higher contrast sensitivity at 6 c.p.d. under photopic conditions, at 6 c.p.d. and 18 c.p.d. under mesopic conditions, and at 6 c.p.d., 12 c.p.d. and 18 c.p.d. under mesopic conditions with glare (p < 0.05). Corneal spherical aberration was 0.273 ± 0.074 μm in the AcrySof Natural group and 0.294 ± 0.086 μm in the AcrySof IQ group (p = 0489). Ocular spherical aberration was 0.362 ± 0.141 μm and 0.069 ± 0.043 μm (p < 0.001) for 6-mm diameter pupils and 0.143 ± 0.091 μm and 0.017 ± 0.016 μm (p < 0.001) for 4-mm diameter pupils, with AcrySof Natural and AcrySof IQ IOLs, respectively. There were no significant differences in other higher-order aberrations between the groups (p > 0.05). Aspherical AcrySof IQ IOLs significantly reduced spherical aberration for pupil diameters of both 4 mm and 6 mm and also improved contrast sensitivity more than spherical AcrySof Natural IOLs, especially in mesopic conditions.

Due to the low spatial resolution of images taken from the Chang'e-1 (CE-1) orbiter, the details of the lunar surface are blurred and lost. Considering the limited spatial resolution of image data obtained by a CCD camera on CE-1, an example-based super-resolution (SR) algorithm is employed to obtain high-resolution (HR) images. SR reconstruction is important for the application of image data to increase the resolution of images. In this article, a novel example-based algorithm is proposed to implement SR reconstruction by single-image analysis, and the computational cost is reduced compared to other example-based SR methods. The results show that this method can enhance the resolution of images using SR and recover detailed information about the lunar surface. Thus it can be used for surveying HR terrain and geological features. Moreover, the algorithm is significant for the HR processing of remotely sensed images obtained by other imaging systems.

The aim of this study was to evaluate the effect of exposure time and image resolution on fractal dimension (FD) of periapical bone on images obtained using a storage phosphor plate (SPP) system. Periapical images of premolar and molar teeth on both sides of ten dry human mandibles were obtained with Digora Optime (Soredex Corp., Helsinki, Finland) SPP system. The SPPs were exposed with three exposure times (0.05, 0.12, and 0.30s) and scanned immediately after exposure with high and super resolutions. FD was calculated using public domain software (ImageJ with FracLac plug-in) on two non-overlapping region of interest (ROIs) on premolar and molar periapical bone areas of each radiograph using differential box-counting method. The ROIs on corresponding images were of the same size and position. FDs were compared using two-way ANOVA and Tukey-Kramer multiple comparison tests (p = 0.05). Images obtained with super resolution scans gave significantly higher FD values compared to high-resolution scanning for all exposures (p < 0.0001). FD values were decreasing with increase in exposure time for both resolutions (p < 0.0001). The highest FD was found for images with super resolution and shortest exposure time, which exhibited the highest noise. FD analysis seems not so robust method as it was believed previously. It shows significant changes with image resolution and exposure time. Exposure time and scanning resolution of SPPs should be carefully chosen when evaluating the change in FD of alveolar bone for various bone disorders.

To validate a normal-resolution (NR) simulation (NRsim) algorithm that uses high-resolution (HR) or super-high resolution (SHR) acquisitions on a commercial HR computed tomography (CT) scanner by comparing image quality between NRsim-generated images and actual NR images. NRsim is intended to allow direct comparison between normal-resolution CT and HR/SHR reconstructions in clinical investigations, without repeating exams. The Aquilion Precision CT (Canon Medical Systems Corporation) HR CT scanner has three resolution modes resulting from detector binning in the channel (x-y) and row (z) directions. For NR, each detector element is 0.5mm×0.5mm along the channel and row directions, 0.25mm×0.5mm for HR, and 0.25mm×0.25mm for SHR. The NRsim algorithm simulates NR acquisitions from HR or SHR acquisitions (termed NRHR and NRSHR , respectively) by downsampling the pre-log raw data in the channel direction for the HR acquisitions and in the channel and row direction for the SHR acquisition. The downsampled data are then reconstructed using the same process as NR. The axial modulation transfer function (MTF), slice sensitivity profile (SSP), and CT number accuracy were measured using the Catphan 600 phantom, and the three-dimensional noise power spectrum (NPS) was measured in water-equivalent phantoms for standard protocols across a range of size-specific dose estimates (SSDE): head (6.2-29.8mGy), lung (2.2-18.2mGy), and body (5.6-19.4mGy). The MTF and NPS measurements were combined to estimate low-contrast detectability (LCD) using a non-prewhitening model observer with an eye filter for a 5-mm disk with 10HU contrast. All metrics were compared for NR, NRHR , and NRSHR images reconstructed using filtered back projection (FBP) and an iterative reconstruction algorithm (AIDR3D). We chose a 15% error threshold as a reasonable definition of success for NRsim when compared against actual NR based on published studies showing that a just-noticeable difference in image noise level for human observers is typically <15%. The axial MTF and SSPs for NRsim were in good agreement with NR demonstrated by a maximum difference of 5.1% for the MTF at 10% and 50% across materials (air, Teflon, LDPE, and polystyrene) and a maximum SSP difference of 2.2%. Noise magnitude differences were within 15% across the SSDE levels with the exception of below 4.5mGy for the lung protocol with FBP. The relative RMSE of normalized NPS comparisons were all <15%. Differences in CT numbers for NRsim reconstructions were within 2HU of NR. LCD for NRsim was within 15% of NR with the exception of NRSHR for the lung protocol SSDE levels below 3.7mGy with FBP. NRsim, an algorithm for simulating NR acquisitions using HR and SHR raw data, was introduced and shown to generate images with spatial resolution, noise, HU accuracy, and LCD largely equivalent to scans acquired using an actual NR acquisition. At SSDE levels below ~5mGy for the lung protocol, differences in noise magnitude and LCD for NRSHR were >15% which defines a region where NRsim degrades due to contributions from electronic noise.

In view of the limitation in term of direction that the existing interpolation reconstruction algorithm of single image and super resolution (SR) image cannot fully utilize the specific geometric features of the image itself, this paper proposed a novel SR algorithm of single image based on the integration of Bandelet geometric-flow-oriented interpolation and total variation in wavelet domain (IBITV). This method seamlessly took advantages of both wavelet and super-wavelet transformations. Firstly, the framework of image SR reconstruction and optimization was presented based on Bandelet and wavelet transformation. The framework made full use of the acquisition ability of the accurately geometric flow characteristics of the image based on Bandelet transformation. Furthermore, it introduced the idea that Bandelet transformed two-dimensional image and one-dimensional signal were projection mapping interactively, and the structures of image are similar at the same high frequency components of different scales after wavelet transformation to apply the Bandelet precise direction discrimination ability rather than sparse representation ability. Finally, in order to avoid the possibility of block effect, the influence of difference between low frequency component of the high resolution image and low resolution image on the final reconstructed image was considered based on the initially estimated image with high resolution. The total variation method of wavelet domain was utilized to carry out iterative solution of SR so as to obtain the final high resolution image. Experimental results show that the proposed method achieved favorable results in both iterative convergence characteristic of the algorithm and the image SR.

Due to the diffraction limit of optical system and detector fabrication technology, super-resolution imaging has been an important means to achieve high spatial resolution in remote sensing. One super-resolution imaging method based on sub-pixels is explored in the paper, the mechanism and image reconstruction of the method are mainly studied. Taking one remote sensor as a sample, registration accuracy of homonymous points, which matters to super-resolution image reconstruction, and its influencing factors, are analyzed, showing that, the image resolution can be improved about 1.5 times when the registration accuracy of homonymous points is better than 0.12 pixels.

Image resolution enhancement is a usable process for many image processing applications such as geoscience studies, astronomy and geographical information systems. One of the traditional methods used to increase the image resolution is image interpolation but the potential problem associated with it is to magnify the image many times without loss in image clarity. However, all the classical linear interpolation techniques like bilinear, bi-cubic interpolation methods generate blurred image. By employing dual-tree complex wavelet transform (DT-CWT) on a edge directional interpolation, it is possible to recover the high frequency components which provides an image with good visual clarity and thus super resolved high resolution images are obtained. The obtained simulation results comply with the above stated claim. A performance comparison of it is made with the recent work discussed in.

Current InP photonic device monolithic integration schemes are discussed and their pros and cons are compared. A novel type of super high resolution (SHR) InP grating is introduced. The monolithic integration of the SHR grating with active devices could lead to a powerful DWDM-on-Chip technology that offers attractive solutions for avionics photonics.

Image super-resolution can be used to satisfy the increasing requirement of people to the high resolution and 3D display of images and videos. Beside to the classical methods, deep learning-based image super-resolution can provide an excellent solution. Spatial-channel specific convolution method is proposed to implement image super resolution, in which both spatial information and channel feature information are used to improve the effect of reconstruction. Adaptive weights and multiplex attention methods are introduced for spatial specific convolution kernels, which is similar to the summation of Graph attention network and self-similarity. Besides, we further improve the similarity calculation method of self-attention in spatial specific convolution by using fully connected layer to introduce nonlinear transform of features, so as to further expand the learning space of convolution kernel and obtain stronger feature extraction ability. Experimental results show the channel and spatial information are helpful to construct high resolution images, and our method can used to be better on getting high-resolution reconstructed images than the previous methods

The single image super resolution recovers missing high resolution details so as to reconstruct a high resolution image from a single low resolution image. This paper proposes a novel directionally adaptive, learning-based, single image super resolution method using multiple direction wavelet transform, called directionlets. Here, critically sampled directionlets are used to capture directional features effectively and to extract edge information along different directions from a set of available high resolution images. This information is used as the training set for super resolving a low resolution input image. The directionlet coefficients at finer scales of its high resolution image are learned locally from this training set and the inverse directionlet transform recovers the super resolved high resolution image. The simulation results showed that the proposed directionlet approach outperforms standard interpolation techniques like cubic spline interpolation as well as standard wavelet-based learning, both visually and in terms of the mean squared error (MSE) values. The SNR scores for cubic spline interpolation, wavelet and directionlet method are 13.6998 dB, 23.8324 dB and 30.8654 dB respectively for Barbara.