Image Size Research Articles

Contextual Learning in Fourier Complex Field for VHR Remote Sensing Images.

Very high-resolution (VHR) remote sensing (RS) image classification is the fundamental task for RS image analysis and understanding. Recently, Transformer-based models demonstrated outstanding potential for learning high-order contextual relationships from natural images with general resolution ( ≈ 224 × 224 pixels) and achieved remarkable results on general image classification tasks. However, the complexity of the naive Transformer grows quadratically with the increase in image size, which prevents Transformer-based models from VHR RS image ( ≥ 500 × 500 pixels) classification and other computationally expensive downstream tasks. To this end, we propose to decompose the expensive self-attention (SA) into real and imaginary parts via discrete Fourier transform (DFT) and, therefore, propose an efficient complex SA (CSA) mechanism. Benefiting from the conjugated symmetric property of DFT, CSA is capable to model the high-order contextual information with less than half computations of naive SA. To overcome the gradient explosion in Fourier complex field, we replace the Softmax function with the carefully designed Logmax function to normalize the attention map of CSA and stabilize the gradient propagation. By stacking various layers of CSA blocks, we propose the Fourier complex Transformer (FCT) model to learn global contextual information from VHR aerial images following the hierarchical manners. Universal experiments conducted on commonly used RS classification datasets demonstrate the effectiveness and efficiency of FCT, especially on VHR RS images. The source code of FCT will be available at https://github.com/Gao-xiyuan/FCT.

Perceptual Thresholds for Radial Optic Flow Distortion in Near-Eye Stereoscopic Displays.

We provide the first perceptual quantification of user's sensitivity to radial optic flow artifacts and demonstrate a promising approach for masking this optic flow artifact via blink suppression. Near-eye HMOs allow users to feel immersed in virtual environments by providing visual cues, like motion parallax and stereoscopy, that mimic how we view the physical world. However, these systems exhibit a variety of perceptual artifacts that can limit their usability and the user's sense of presence in VR. One well-known artifact is the vergence-accommodation conflict (VAC). Varifocal displays can mitigate VAC, but bring with them other artifacts such as a change in virtual image size (radial optic flow) when the focal plane changes. We conducted a set of psychophysical studies to measure users' ability to perceive this radial flow artifact before, during, and after self-initiated blinks. Our results showed that visual sensitivity was reduced by a factor of 10 at the start and for ~70 ms after a blink was detected. Pre- and post-blink sensitivity was, on average, ~O.15% image size change during normal viewing and increased to ~1.5- 2.0% during blinks. Our results imply that a rapid (under 70 ms) radial optic flow distortion can go unnoticed during a blink. Furthermore, our results provide empirical data that can be used to inform engineering requirements for both hardware design and software-based graphical correction algorithms for future varifocal near-eye displays. Our project website is available at https://gamma.umd.edu/ROF/.

Empowering Ovarian Cancer Subtype Classification with Parallel Swin Transformers and WSI Imaging

Ovarian cancer constitutes a notable proportion of cancer-related mortalities among women. The diagnostic classification of ovarian cancer subtypes has demonstrated complexity, characterized by limited concordance among pathologists. Vision Transformer (ViT) models have emerged as the predominant architecture in numerous computer vision applications, encompassing tasks such as image classification and cancer detection. Their success stems primarily from their capacity to integrate global contextual information through self-attention mechanisms during the learning process. However, the key issue with ViT is its compatibility with high-res images. Computation grows quadratically with image size, resulting in a large number of tokens and significant computational demands for self-attention. Swin Transformer (Swin-T) addresses this challenge by introducing two main concepts: hierarchical feature mapping and windowed attention transformation. This work presents a parallel implementation of Swin Transformers (Swin-Ts) that leverages the powerful feature extraction capabilities and aimed at classifying five subtypes within ovarian cancer utilizing Whole Slide Imaging (WSI) and it yielded average precision, recall, and F1-score metrics of 0.958, 0.964, and 0.96 correspondingly. The findings show that the proposed parallel Swin-Ts reduce the misclassification errors and improve medical image analysis robustness. Additionally, the suggested technique is promising for accurate and efficient ovarian carcinoma subtype categorization, with possible applicability to other cancers. Future research will integrate other data sources and validate the technique in various clinical contexts

Security analysis of a reversible data hiding scheme in encrypted images by redundant space transfer

Redundant space transfer (RST) encryption can effectively enhance the security and embedding capacity of reversible data hiding in encrypted images. To demonstrate the security risks of RST encryption in the cloud environment, according to the known plaintext image and marked encrypted image(MEI) pairs, this paper proposes a known plaintext attack (KPA) based on hamming-weight of bit block. The proposed KPA method includes two stages. The first stage is to find the correct ciphertext image. For possible block sizes, all possible ciphertext images of MEI are reconstructed by deleting the embedded secret data. The Hamming weight image (HWI) of plaintext and all possible ciphertext images is calculated in m×8n domain (m, n is the image size). When the histogram distance between the plaintext HWI and a possible ciphertext HWI is the minimum, the correct ciphertext image is found. The second stage is to estimate the permutation keys. The bit block characteristic matrix (BBCM) is defined by using that the bit values of plaintext and ciphertext remain unchanged in the process of RST encryption. Based on the intersection of BBCM’s index set, the block permutation key is directly estimated. Then, the bit plane permutation key in each image block is further estimated. Experimental results show that for RST encryption, when the block size is not less than 3 × 3, the block permutation and bit plane permutation key can be estimated in less than 3 min by using one pair of plain–ciphertext. The estimation accuracy of block permutation key is more than 50%, and the estimation accuracy of bit plane permutation key is 10% for Qin’s RST algorithm, most of the content information of the ciphertext image is leaked.

Sparse Non-Local CRF With Applications.

CRFs model spatial coherence in classical and deep learning computer vision. The most common CRF is called pairwise, as it connects pixel pairs. There are two types of pairwise CRF: sparse and dense. A sparse CRF connects the nearby pixels, leading to a linear number of connections in the image size. A dense CRF connects all pixel pairs, leading to a quadratic number of connections. While dense CRF is a more general model, it is much less efficient than sparse CRF. In fact, only Gaussian edge dense CRF is used in practice, and even then with approximations. We propose a new pairwise CRF, which we call sparse non-local CRF. Like dense CRF, it has non-local connections, and, therefore, it is more general than sparse CRF. Like sparse CRF, the number of connections is linear, and, therefore, our model is efficient. Besides efficiency, another advantage is that our edge weights are unrestricted. We show that our sparse non-local CRF models properties similar to that of Gaussian dense CRF. We also discuss connections to other CRF models. We demonstrate the usefulness of our model on classical and deep learning applications, for two and multiple labels.

On-the-move heterogeneous face recognition in frequency and spatial domain using sparse representation.

Heterogeneity of a probe image is one of the most complex challenges faced by researchers and implementers of current surveillance systems. This is due to existence of multiple cameras working in different spectral ranges in a single surveillance setup. This paper proposes two different approaches including spatial sparse representations (SSR) and frequency sparse representation (FSR) to recognize on-the-move heterogeneous face images with database of single sample per person (SSPP). SCface database, with five visual and two Infrared (IR) cameras, is taken as a benchmark for experiments, which is further confirmed using CASIA NIR-VIS 2.0 face database with 17580 visual and IR images. Similarity, comparison is performed for different scenarios such as, variation of distances from a camera and variation in sizes of face images and various visual and infrared (IR) modalities. Least square minimization based approach for finding the solution is used to match face images as it makes the recognition process simpler. A side by side comparison of both the proposed approaches with the state-of-the-art, classical, principal component analysis (PCA), kernel fisher analysis (KFA) and coupled kernel embedding (CKE) methods, along with modern low-rank preserving projection via graph regularized reconstruction (LRPP-GRR) method, is also presented. Experimental results suggest that the proposed approaches achieve superior performance.

Noise immunity algorithm for embedding a digital watermark in medical images

When storing and transferring printed medical materials, such as tomograms or radiographs, there is a need to protect additional information from unauthorized access. This information includes personal data of the patient and a summary of the medical history, and it can be added to the medical image (container) in the form of a watermark. Existing algorithms for embedding digital watermarks (DWM) in graphic objects distort the initial characteristics of the container image, which in the case of medical images can lead to a misdiagnosis. This study aimed to develop a distortion- and noise-resistant algorithm for embedding the DWM in the spatial domain of a medical image intended for storage on paper (as a printout). The article initially considered the possibilities of using the method of modifying the least significant bits of image pixel brightness or the LSB algorithm. Mathematical modeling in Matlab showed that the maximum brightness of the DWM guaranteeing its invisibility cannot exceed 2% of the maximum brightness of the container image for monochrome images, and 6% for color images. The maximum value of the white noise dispersion, at which it is possible to single out a DWM with a correlation coefficient of at least 0.9 was 0.0001. We prorose a new noise immunе algorithm (NIA) for embedding the DWM in the subpixels of the main image, which, after extracting the DWM, are not used to build the container image. In the absence of noise, there are no distortions of the original medical image whatsoever. The essence of the NIA is as follows. The size of the original image in the form of a two-dimensional array is quadrupled by adding a subpixel in each row and column with a brightness equal to the average arithmetic brightness of neighboring pixels. A DWM with the same size as the original image is added to the resulting subpixels. Matlab modeling showed that the DWM would remain invisible at a relative brightness of approximately 5% for monochrome container images and 15% for color images. For the NIA algorithm, the maximum value of the white noise dispersion to obtain a correlation coefficient of 0.9 is 0.005, which means that the noise immunity of the proposed method is significantly higher than that of the LSB-based algorithm.

Automatic fabrication system of optical micro-nanofiber based on deep learning

<sec>The wide range, high precision, and dynamic real-time measurement of micro-/nanofiber diameter are crucial for achieving low loss transmission and controlling dispersion in the preparation process of micro-nanofiber. In view of the problems of small diameter regulation range, complex operation and long-time consumption of the existing preparation methods, the automatic detection system of micro-nanofiber is realized based on deep learning neural network algorithm in this work. The image segmentation method in computer vision is used to make high-quality multi-scale micro-/nanofiber datasets, and the improved YOLOv8-FD (You Only Look Once version 8-Fiber Detection) algorithm based on small target detection is used to automatically detect the diameter of micro-nanofiber.</sec><sec>Through image segmentation and identification of the target of single pixel size in the microscopic image, the diameter detection of micro-nanofiber is finally realized. In this process, the real-time diameter of micro-nanofiber is obtained through image information, and then the micro-nanofiber small target is accurately segmented to achieve the precise detection of mAP<sup>IoU</sup><sup>=50</sup> = 0.975 and mAP<sup>IoU</sup><sup>=50—95</sup> = 0.765 on the micro-nanofiber multi-scale target dataset with extremely high accuracy. The algorithm-based construction of a high-precision micro-nanofiber automatic preparation system enables real-time accurate segmentation of fiber edges, calculation of fiber diameter, and feedback to the control system for achieving automated preparation of fibers with arbitrary diameters. Additionally, it facilitates the detection of micro-nanofiber in a range from 462 nm to 125 μm. The average response time for reasoning is 9.6 ms, and the detection error is kept below 2.95%.</sec><sec>In addition, compared with other micro-/nanofiber diameter detection methods based on optical imaging and mode cutoff, this method shows advantages of high precision, high speed and arbitrary diameter preparation for diameter detection based on deep learning neural networks. The system is very suitable for high-precision real-time measurement and automatic and accurate preparation of micro-/nanofibers, thereby providing a novel method of developing micro-nanofiber devices with low-loss transmission and adjustable dispersion. </sec>

Improving Visualization of In-stent Lumen Using Prototype Photon-counting Detector Computed Tomography with High-resolution Plaque Kernel.

The study aimed to compare the performance of photon-counting detector computed tomography (PCD CT) with high-resolution (HR)-plaque kernel with that of the energy-integrating detector CT (EID CT) in terms of the visualization of the lumen size and the in-stent stenotic portion at different coronary vessel angles. The lumen sizes in PCD CT and EID CT images were 2.13 and 1.80 mm at 0°, 2.20 and 1.77 mm at 45°, and 2.27 mm and 1.67 mm at 90°, respectively. The lumen sizes in PCD CT with HR-plaque kernel were wider than those in EID CT. The mean degree of the in-stent stenotic portion at 50% was 69.7% for PCD CT and 90.4% for EID CT. PCD CT images with HR-plaque kernel enable improved visualization of lumen size and accurate measurements of the in-stent stenotic portion compared to conventional EID CT images regardless of the stent direction.

Comparación de dimensiones dentales en modelos desarrollados con procedimientos digitales y modelos en yeso

Aim: This study aimed to collect evidence on the validity and reliability of measurements obtained from digital impression techniques. Materials and Methods: This comparative study was conducted on 31 patients. Intraoral scanner was applied to all patients. For each patient, an alginate impression of the upper maxilla was taken and later the 3D digital model was extracted by dental cone-beam computed tomography (CBCT). For preparation of plaster models, alginate impressions were taken and immediately poured with dental stone. In the next stage, a comparison was performed among the intraoral scanner, CBCT, and plaster models in terms of tooth size, dental width, and intra-arch dimensions. Results: Measuring tooth size and intra-arch dimensions in digital images obtained from intraoral scanner and CBCT were in most cases lower than the results obtained in the plaster models but the differences between digital techniques and plaster models are not clinically noticeable. Conclusions: Digital systems including intraoral scanner and CBCT are acceptable for clinical use in terms of accuracy.

Accurate Lung Nodule Segmentation With Detailed Representation Transfer and Soft Mask Supervision.

Accurate lung lesion segmentation from computed tomography (CT) images is crucial to the analysis and diagnosis of lung diseases, such as COVID-19 and lung cancer. However, the smallness and variety of lung nodules and the lack of high-quality labeling make the accurate lung nodule segmentation difficult. To address these issues, we first introduce a novel segmentation mask named "soft mask", which has richer and more accurate edge details description and better visualization, and develop a universal automatic soft mask annotation pipeline to deal with different datasets correspondingly. Then, a novel network with detailed representation transfer and soft mask supervision (DSNet) is proposed to process the input low-resolution images of lung nodules into high-quality segmentation results. Our DSNet contains a special detailed representation transfer module (DRTM) for reconstructing the detailed representation to alleviate the small size of lung nodules images and an adversarial training framework with soft mask for further improving the accuracy of segmentation. Extensive experiments validate that our DSNet outperforms other state-of-the-art methods for accurate lung nodule segmentation, and has strong generalization ability in other accurate medical segmentation tasks with competitive results. Besides, we provide a new challenging lung nodules segmentation dataset for further studies (https://drive.google.com/file/d/15NNkvDTb_0Ku0IoPsNMHezJR TH1Oi1wm/view?usp=sharing).

Цифровой сглаживающий трапецеидальный рекурсивно-сепарабельный фильтр обработки изображений с изменяемыми размерами сканирующей многоэлементной апертуры

The development of television systems is an important factor for many industries involved in the acquisition, processing, storage and transmission of images. Today, an urgent task in the use of such systems is to improve the quality of images obtained using digital photo and video cameras. To solve this problem, digital recursive-separable smoothing filters can be used. The paper describes the process of operation of the algorithm for changing the size of the scanning multi-element aperture of a smoothing trapezoidal recursive-separable filter for digital image processing. The results of evaluating its performance relative to the same algorithm implemented through classical two-dimensional convolution for various sizes of test images are presented. The influence of the aperture size of the developed filter on the change in the signal-to-noise ratio is assessed. The algorithm is implemented in the MATLAB computing environment.

Unleash the Power of State Space Model for Whole Slide Image with Local Aware Scanning and Importance Resampling.

Whole slide image (WSI) analysis is gaining prominence within the medical imaging field. However, previous methods often fall short of efficiently processing entire WSIs due to their gigapixel size. Inspired by recent developments in state space models, this paper introduces a new Pathology Mamba (PAM) for more accurate and robust WSI analysis. PAM includes three carefully designed components to tackle the challenges of enormous image size, the utilization of local and hierarchical information, and the mismatch between the feature distributions of training and testing during WSI analysis. Specifically, we design a Bi-directional Mamba Encoder to process the extensive patches present in WSIs effectively and efficiently, which can handle large-scale pathological images while achieving high performance and accuracy. To further harness the local information and inherent hierarchical structure of WSI, we introduce a novel Local-aware Scanning module, which employs a local-aware mechanism alongside hierarchical scanning to adeptly capture both the local information and the overarching structure within WSIs. Moreover, to alleviate the patch feature distribution misalignment between training and testing, we propose a Test-time Importance Resampling module to conduct testing patch resampling to ensure consistency of feature distribution between the training and testing phases, and thus enhance model prediction. Extensive evaluation on nine WSI datasets with cancer subtyping and survival prediction tasks demonstrates that PAM outperforms current state-of-the-art methods and also its enhanced capability in modeling discriminative areas within WSIs. The source code is available at https://github.com/HKU-MedAI/PAM.

Using kernel functions to estimate the probability density function for segmentation images

This paper used nonparametric kernel functions to segment images using the thresholding method. This was done by transforming the image to grey and then estimating the probability density function from the grey image data and taking the highest value of the core function vector as the threshold limit. The study also found that the bandwidth parameter affects the segmentation process because it affects the estimate of the probability density function. The bandwidth parameter smooths the curve and brings it closer to the true curve because of its significant effect on bias and variance. The beam width parameter is known to depend on the size of the sample. Thus, it was found that when the parameter is used to determine the image size into equal rows and columns, it gives good, satisfactory results for segmented images that contain the most important areas of interest with removing unhelpful or important areas. Finally, the uniform Gaussian, cosine, triangular, and logistic Silverman kernel functions proved their efficiency in extracting all image features.

Deep learning model for differentiating acute myeloid and lymphoblastic leukemia in peripheral blood cell images via myeloblast and lymphoblast classification.

Acute leukemia (AL) is a life-threatening malignant disease that occurs in the bone marrow and blood, and is classified as either acute myeloid leukemia (AML) or acute lymphoblastic leukemia (ALL). Diagnosing AL warrants testing methods, such as flow cytometry, which require trained professionals, time, and money. We aimed to develop a model that can classify peripheral blood images of 12 cell types, including pathological cells associated with AL, using artificial intelligence. We acquired 42,386 single-cell images of peripheral blood slides from 282 patients (82 with AML, 40 with ALL, and 160 with immature granulocytes). The performance of EfficientNet-V2 (B2) using the original image size exhibited the greatest accuracy (accuracy, 0.8779; precision, 0.7221; recall, 0.7225; and F1 score, 0.7210). The next-best accuracy was achieved by EfficientNet-V1 (B1), with a 256 × 256 pixels image. F1 score was the greatest for EfficientNet-V1 (B1) with the original image size. EfficientNet-V1 (B1) and EfficientNet-V2 (B2) were used to develop an ensemble model, and the accuracy (0.8858) and F1 score (0.7361) were improved. The classification performance of the developed ensemble model for the 12 cell types was good, with an area under the receiver operating characteristic curve above 0.9, and F1 scores for myeloblasts and lymphoblasts of 0.8873 and 0.8006, respectively. The performance of the developed ensemble model for the 12 cell classifications was satisfactory, particularly for myeloblasts and lymphoblasts. We believe that the application of our model will benefit healthcare settings where the rapid and accurate diagnosis of AL is difficult.

Efficient-VGG16: A Novel Ensemble Method for the Classification of COVID-19 X-ray Images in Contrast to Machine and Transfer Learning

Corona Virus Disease 2019 (COVID-19) is a contagious respiratory disease characterized by its high transmissibility and exponential spread, presenting persistent difficulties. This investigation sought to incorporate machine learning (ML) and transfer learning (TL) and introduced Efficient-VGG16, a novel ensemble approach that integrated the EfficientNet-B0 and VGG16 architectures to classify chest X-ray images as either COVID-19 positive or non-COVID. The COVID-Xray-5k dataset used in the research was split into different training and testing sets (with ratios of 80:20, 75:25, and 70:30), preprocessed, and then used to evaluate the approach’s effectiveness. Throughout the investigation, all procedures operated effectively at the same selected image size, 224*224. Preprocessing included Gaussian blur to remove noise and relics, while Contrast Limited Adaptive Histogram Equalization (CLAHE) adjusted contrast. Among the employed ML classifiers, XgBoost attained the highest accuracy, with F1 scores of 95.78% and 71.56%, respectively. In addition, the research investigated the effectiveness of TL using pre-trained convolutional neural networks (CNNs) such as VGG16, MobileNetV3, ResNet101, Densenet201, and EfficientNet-B0. EfficientNet-B0 achieved the highest levels of accuracy and F1 score, 99.31% and 93.72%, respectively. Furthermore, the proposed Efficient-VGG16 achieved accuracy and an F1 score of 99.46% and 98.41%, respectively, and outperformed TL with EfficientNet-B0 and XgBoost. The investigation demonstrated the effectiveness of the Efficient-VGG16 for accurately classifying COVID-19.

Robust Image Watermarking using DWT, DCT, and PSO with CNN-Based Attack Evaluation

Digital Content Protection is one of the most significant research areas that lies at the intersection of cyber-security and multimedia processing. Protect multimedia content from copyright violation, unauthorized use, replication, and online content theft is needed. Digital Image Watermarking is utilized to preserve the copyright of different digital images from forgery. Various techniques had been developed in this regard with two main issues, the method robustness and the resistance against various types of attacks like, Salt and Pepper noise, filtering and blurring. Current Digital watermarking techniques may reduce the quality of the original digital media content if it is not robust. The purpose of this research is to create a robust image watermarking technique against different attack types such as salt and pepper noise and Gaussian noise, ensuring the image content is protected. Specifically, this study proposed a new mechanism for Image watermarking based on combining Discrete Wavelet Transform (DWT), and Discrete Cosine Transform (DCT). Additionally, Particle Swarm Optimization (PSO) was applied to perform the optimization for both the embedding and extraction processes. At the final stage we assess the proposed approach against some types of attacks such as Additive White Gaussian Noise (AWGN). The Denoising Convolutional Neural Network, (DnCNN) was used to evaluate the mechanism against AWGN. For testing, we utilized measures such as Peak Signal-to-Noise Ratio (PSNR) and Normalized Correlation Coefficient (NCC). The experimental results of implementing our proposed method for embedding and extracting watermarks across various host image sizes were encouraging, achieving a PSNR ratio of 0.998 and an NCC of 1 in the absence of attacks. Additionally, our evaluation revealed that a specific type of denoising attack, while damaging the watermark (though not completely), actually improved the image quality. It is also important to highlight that our findings surpassed those reported in existing literature, with the PSNR and NCC values serving as evidence of this superior performance.

A Contour-Based Feature Extraction Algorithm

Human body recognition is a common problem in computer vision. Currently, most artificial intelligence methods are dependent on the scale of the extracted feature values from the target images during classification, thereby limiting the image size. However, in image recognition, the object’s contour alone is sufficient for humans to distinguish image content. This paper proposes a feature extraction algorithm based on image contours. By utilizing only the contour features of the target image and combining them with machine learning algorithms for classification, it achieves good results even on relatively complex images.

A Modified Xception Deep Learning Model for Automatic Sorting of Olives Based on Ripening Stages

Olive fruits at different ripening stages give rise to various table olive products and oil qualities. Therefore, developing an efficient method for recognizing and sorting olive fruits based on their ripening stages can greatly facilitate post-harvest processing. This study introduces an automatic computer vision system that utilizes deep learning technology to classify the ‘Roghani’ Iranian olive cultivar into five ripening stages using color images. The developed model employs convolutional neural networks (CNN) and transfer learning based on the Xception architecture and ImageNet weights as the base network. The model was modified by adding some well-known CNN layers to the last layer. To minimize overfitting and enhance model generality, data augmentation techniques were employed. By considering different optimizers and two image sizes, four final candidate models were generated. These models were then compared in terms of loss and accuracy on the test dataset, classification performance (classification report and confusion matrix), and generality. All four candidates exhibited high accuracies ranging from 86.93% to 93.46% and comparable classification performance. In all models, at least one class was recognized with 100% accuracy. However, by taking into account the risk of overfitting in addition to the network stability, two models were discarded. Finally, a model with an image size of 224 × 224 and an SGD optimizer, which had a loss of 1.23 and an accuracy of 86.93%, was selected as the preferred option. The results of this study offer robust tools for automatic olive sorting systems, simplifying the differentiation of olives at various ripening levels for different post-harvest products.

Sinogram Interpolation Inspired by Single-Image Super Resolution

Computed tomography is a medical imaging procedure used to estimate the interior of a patient or an object. Radiation scans are taken at regularly spaced angles around the object, forming a sinogram. This sinogram is then reconstructed into an image representing the contents of the object. This results in a fair amount of radiation exposure for the patient, which increases the risk of cancer. Less radiation and fewer views, however, leads to inferior image reconstruction. To solve this sparse-view problem, a deep-learning model is created that takes as input a sparse sinogram and outputs a sinogram with interpolated data for additional views. The architecture of this model is based on the super-resolution convolutional neural network. The reconstruction of modelinterpolated sinograms has less mean-squared error than the reconstruction of the sparse sinogram. It also has less mean-squared error than a reconstruction of a sinogram interpolated using the popular bilinear image-resizing algorithm. This model can be easily adapted to different image sizes, and its simplicity translates into efficiency in both time and memory requirements.