Implementation of ill-sampled image geometry super-resolution processing technology

Causes of the apparent scale independence of fractal indices associated with forest fragmentation in Bolivia

Optical imaging resolution is determined by both optical parameters such as light wavelength and the objective lens Numerical Aperture (NA) and by spatial sampling such as Charge-Coupled Device (CCD) camera pixel size. Focused on improving optical imaging pixel resolution, we reviewed multiframe Super-Resolution (SR) implemented in previous measurement to improve image resolution, but found its computational cost to high and calculation too long. Pixeldisplacement estimation also adversely affects resulting image quality and is difficult to apply practically due to SR registration calculation sensitivity to noise. The resolution improvement we have proposed uses active subpixel shifting of the optical axis based on actively controlling spatial image displacement using a glass-plate-parallel substrate and a galvano scanner. Multiframe SR registration is used but without pixel-displacement estimation. Theoretically, our proposal may improve image resolution stably at higher speed but to clarify this, we developed optics and rebuilt the multiframe SR.We then computationally analyzed improved resolution and noise tolerance using a Modulation Transfer Function (MTF). Optical and aliasing noise have been suppressed and image resolution improved in high measurement, making higherresolution imaging faster and cheaper thanks to our proposal.

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.

The size of pixels in a digital recording device, such as a CCD array, limits the spatial resolution in images obtained by an optical imaging system, thereby degrading the image quality. Digital super-resolution (DSR) techniques are used to reconstruct a high-resolution (HR) image from multiple sub-pixel-shifted low-resolution images in order to improve the image quality. In this article, we formulate a mathematical framework for DSR using the generalized sampling theorem (GST). The GST-based DSR method’s performance is evaluated by comparing it to existing resolution enhancement methods on the basis of evaluation metrics like percentage mean square error (%MSE), structural similarity index measure (SSIM), and peak signal-to-noise ratio (PSNR). The GST DSR method exhibits an overall superior quality image reconstruction based on quantitative analysis (with a near zero %MSE, SSIM around one, and improved PSNR in dB) and qualitative comparison. The robustness of the GST DSR method is further demonstrated in the presence of frame-to-frame shift estimation error using %MSE and SSIM by comparing it with multi-frame interpolation approaches.

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 resolution of far-field optical imaging is required to improve beyond the Abbe limit to the subdiffraction or even the nanoscale. In this work, inspired by scanning electronic microscopy (SEM) imaging, in which carbon (or Au) thin films are usually required to be coated on the sample surface before imaging to remove the charging effect while imaging by electrons. We propose a saturation-absorption-induced far-field super-resolution optical imaging method (SAI-SRIM). In the SAI-SRIM, the carbon (or Au) layers in SEM imaging are replaced by nonlinear-saturation-absorption (NSA) thin films, which are directly coated onto the sample surfaces using advanced thin film deposition techniques. The surface fluctuant morphologies are replicated to the NSA thin films, accordingly. The coated sample surfaces are then imaged using conventional laser scanning microscopy. Consequently, the imaging resolution is greatly improved, and subdiffraction-resolved optical images are obtained theoretically and experimentally. The SAI-SRIM provides an effective and easy way to achieve far-field super-resolution optical imaging for sample surfaces with geometric fluctuant morphology characteristics.

A new optical system with a resolution that is superior to the resolution of the usual optical systems with diffraction limit is presented. We introduce a newly generated narrow light beam that propagates for a long range while almost maintaining its beam width and show that the beam width is narrower than that of the diffraction limit of normal optics. Thus a super high resolution is achieved for a long range, e.g., a range of a few kilometers, by the use of a 10-cm-diameter telescope. The high resolution for long-range imaging can be obtained by a Galilean telescope with a negative eyepiece that has a spherical aberration. We demonstrate theoretically high-resolution imaging by using simple objects and assuming a telescope 10 cm in diameter and a visible wavelength. A comparison of simulation results by the conventional optical system and by the special optical system clearly shows the superiority of the new system.

Limited resolution is one of the most important factors hindering the application of remote sensing images (RSIs). Single-image super resolution (SISR) is a technique to improve the spatial resolution of digital images and has attracted the attention of many researchers. In recent years, with the advancement of deep learning (DL) frameworks, many DL-based SISR models have been proposed and achieved state-of-the-art performance; however, most SISR models for RSIs use the bicubic downsampler to construct low-resolution (LR) and high-resolution (HR) training pairs. Considering that the quality of the actual RSIs depends on a variety of factors, such as illumination, atmosphere, imaging sensor responses, and signal processing, training on “ideal” datasets results in a dramatic drop in model performance on real RSIs. To address this issue, we propose to build a more realistic training dataset by modeling the degradation with blur kernels and imaging noises. We also design a novel residual balanced attention network (RBAN) as a generator to estimate super-resolution results from the LR inputs. To encourage RBAN to generate more realistic textures, we apply a UNet-shape discriminator for adversarial training. Both referenced evaluations on synthetic data and non-referenced evaluations on actual images were carried out. Experimental results validate the effectiveness of the proposed framework, and our model exhibits state-of-the-art performance in quantitative evaluation and visual quality. We believe that the proposed framework can facilitate super-resolution techniques from research to practical applications in RSIs processing.

Super-resolution (SR) techniques present a suitable solution to increase the image resolution acquired using an ultrasound device characterized by a low image resolution. This can be particularly beneficial in low-resource imaging settings. This work surveys advanced SR techniques applied to enhance the resolution and quality of fetal ultrasound images, focusing Dual back-projection based internal learning (DBPISR) technique, which utilizes internal learning for blind super-resolution, as opposed to blind super-resolution generative adversarial network (BSRGAN), real-world enhanced super-resolution generative adversarial network (Real-ESRGAN), swin transformer for image restoration (SwinIR) and SwinIR-Large. The dual back-projection approach enhances SR by iteratively refining downscaling and super-resolution processes through a dual network training method, achieving high accuracy in kernel estimation and image reconstruction. Real-ESRGAN uses synthetic data to simulate complex real-world degradations, incorporating a U-shaped network (U-Net) discriminator to improve training stability and visual performance. BSRGAN addresses the limitations of traditional degradation models by introducing a realistic and comprehensive degradation process involving blur, downsampling, and noise, leading to superior real-world SR performance. Swin models (SwinIR and SwinIR_large) employ a Swin Transformer architecture for image restoration, excelling in capturing long-range dependencies and complex structures, resulting in an outstanding performance in PSNR, SSIM, NIQE, and BRISQUE metrics. The tested images, sourced from five developing countries and often of lower quality, enabled us to show that these approaches can help enhance the quality of the images. Evaluations on fetal ultrasound images reveal that these methods significantly enhance image quality, with DBPISR, Real-ESRGAN, BSRGAN, SwinIR, and SwinIR-Large showing notable improvements in PSNR and SSIM, thereby highlighting their potential for improving the resolution and diagnostic utility of fetal ultrasound images. We evaluated the five aforementioned Super-Resolution models, analyzing their impact on both image quality and classification tasks. Our findings indicate that these techniques hold great potential for enhancing the evaluation of medical images, particularly in development countries. Among the models tested, Real-ESRGAN consistently enhanced both image quality and diagnostic accuracy, even when challenged by limited and variable datasets. This finding was further supported by deploying the ConvNext-base classifier, which demonstrated improved performance when applied to the super-resolved images. Real-ESRGAN’s capacity to enhance image quality, and in turn, classification accuracy, highlights its potential to address the resource constraints often encountered in these settings.

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.

Structured illumination microscopy (SIM) is one of the most powerful and versatile super-resolution methods due to its live-cell imaging ability of subcellular structures with high speed. In this paper, we propose an alternative SIM assisted by Bloch surface wave (BSW). Through replacing the conventional laser interference fringes with sub-diffraction BSW counterparts with higher spatial frequency, the imaging resolution of SIM could be enhanced greatly and the super-resolution imaging capability down to 80&#x00A0;nm could be achieved. Compared with traditional wide-field fluorescence microscopy, this BSW SIM demonstrates 2.78 times enhancement in imaging resolution, which surpasses general SIM with only 2 times improvement. Moreover, the structured illumination intensity could be boosted drastically, which is beneficial to nonlinear super-resolution imaging techniques, such as saturated SIM. This BSW SIM would provide a super-resolution, wide field of view approach for surface super-resolution fluorescent imaging while maintaining high imaging speed and good bio-compatibility.

Influence of refractive index contrast between microsphere lens and immersion medium on imaging resolution

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.

Super-resolution optical fluctuation imaging (SOFI) is a fast and low-cost live-cell optical nanoscopy for extracting subdiffraction information from the statistics of fluorescence intensity fluctuation. As SOFI is based on the fluctuation statistics, rather than the detection of single molecules, it poses unique requirements for imaging detectors, which still lack a systematic evaluation. Here, we analyze the influences of pixel sizes, frame rates, noise levels, and different gains in SOFI with simulations and experimental tests. Our analysis shows that the smaller pixel size and faster readout speed of scientific-grade complementary metal oxide semiconductor (sCMOS) enables SOFI to achieve high spatiotemporal resolution with a large field-of-view, which is especially beneficial for live-cell super-resolution imaging. Overall, as the performance of SOFI is relatively insensitive to the signal-to-noise ratio (SNR), the gain in pixel size and readout speed exceeds the loss in SNR, indicating sCMOS is superior to electron multiplying charge coupled device in context to SOFI in many cases. Super-resolution imaging of cellular microtubule structures with high-order SOFI is experimentally demonstrated at large field-of-view, taking advantage of the large pixel number and fast frame rate of sCMOS cameras.

In this paper, we proposed improved single image super resolution using multiple re-evaluation Process model for use in various image processing fields. The proposed method generates the first super resolution using the input image, and analyzes the change for each region by comparing the features of previous image and super resolution result. According to the analyzed features, the feature map for generate n-th super resolution was selected for improved detail. After then, next generate super resolution using previous super resolution result as input image. This process is repeated for final result. The existing single image super resolution method has two areas to be improved. First, it minimizes artifacts or staircases, which are unnecessary details that can be created during the super resolution process. Second, it is necessary to consider the input image because it affects the result depending on the quality of input image used in the super resolution process. Therefore, in order to minimize unnecessary details, the proposed method analyzed the feature map from the generated super resolution result and applied it according to the amount of change. In addition, aimed to gradually improve the input image to be used in the super resolution process by using the super resolution generated in the previous step. By comparing and evaluating the proposed method with the conventional single image super resolution method with PSNR and SSIM, it is improved by about 3%.