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Recently, several high-performance instance segmentation models have used the Mask R-CNN model as a baseline, which reached a historical peak in instance segmentation in 2017. There are numerous derived models using the Mask R-CNN model, and if the performance of Mask R-CNN is improved, the performance of the derived models is also anticipated to improve. The Mask R-CNN uses interpolation to adjust the image size, and the input differs depending on the interpolation method. Therefore, in this study, the performance change of Mask R-CNN was compared when various interpolation methods were applied to the transform layer to improve the performance of Mask R-CNN. To train and evaluate the models, this study utilized the PennFudan and Balloon datasets and the AP metric was used to evaluate model performance. As a result of the experiment, the derived Mask R-CNN model showed the best performance when bicubic interpolation was used in the transform layer. [ABSTRACT FROM AUTHOR]

Low-Resolution Thermopile Array Sensors are widely used in several indoor applications such as security, intelligent surveillance, robotics, military, and health monitoring systems. It is compact, cost-effective, and offers a low-resolution thermal image of the environment, attracting its use in privacy-focused applications. Many industries migrating towards Industry 4.0 are facing challenges in using sensors and automating the systems. One of the areas in which automation could be implemented is by using sensors to operate the systems smartly based on occupancy. The major challenge in such applications is maintaining privacy; conventional imaging mechanisms using optical camera systems fail to achieve it. The same could be achieved by using thermopile sensors which provide thermal data of the desired region. This generates the possibility to identify the number of people in a specified area without revealing their identity. This paper proposes various approaches to detect human occupancy using a low-resolution infrared thermopile array sensor to keep their identity safe and avoid privacy issues. The proposed system detects IR-emitting objects using a low-resolution thermopile array Grid-EYE sensor (AMG8833). The sensor acquires $8\times 8$ pixels of thermal distribution. These thermal distribution data are subjected to interpolation, filtering, adaptive thresholding, and background suppression to attain the set goal of human detection.

Multimedia Super-Resolution (SR) reconstruction is an essential and mandatory process for different visualization functions. Recently, several schemes have been suggested for single- and multi-image SR reconstruction. This work presents an effective SR reconstruction scheme for visual quality and resolution enhancement of 3D Video (3DV) sequences. The idea behind the proposed 3DV SR reconstruction scheme is the utilization of a recursive Bayesian algorithm for improving the resolution of the degraded 3DV sequences with down-sampling, blurring, and noise effects. In addition, a significant stage of histogram matching based on a visual image with a better-distributed histogram is employed. The main aim of employing the histogram matching stage for enhancing the 3DV sequence is to introduce a dynamic range modification of each 3DV frame. Hence, it presents a 3DV sequence with an enhanced distribution of intensities. This improves the whole performance efficiency of the suggested scheme. The performance of the proposed SR reconstruction scheme is compared with that of the conventional bicubic interpolation scheme. Comparisons with recent and related SR reconstruction schemes are also introduced. Simulation results reveal that the proposed scheme achieves superior outcomes in terms of Structural Similarity (SSIM) index, local contrast, average gradient, Mean Square Error (MSE), edge intensity, entropy, and Peak Signal-to-Noise Ratio (PSNR) of the resulting 3DV frames.

Satellites provide global long-time series of spatio-temporal continuous CO2 observations. However, it is difficult to be applied to the study of small-scale carbon cycle because of its low spatial resolution. In this paper, the Greenhouse Gases Observing SATellite (GOSAT) XCO2 data are super-resolution reconstructed using bicubic interpolation, which improved the spatial resolution from 2.5o to 0.5o. CO2 measurements from ten selected TCCON sites are used to compare with the reconstructed GOSAT. Further, the high accuracy Orbiting Carbon Observatory-2 (OCO-2) data analysed by the combination of geographical grid statistics and kriging is used to evaluate the reconstructed data. The results show that compared with the original GOSAT data, the reconstructed GOSAT data not only improves the spatial resolution but also has little loss of the average accuracy. The mean error of original data has significant seasonal fluctuations with a peak from February to March and a trough from June to July [ABSTRACT FROM AUTHOR]

This paper proposes an image interpolation method with regional gradient estimation (GEI) to solve the problem of the nonlinear interpolation method not sufficiently considering non-edge pixels. First, the approach presented in this paper expanded on the edge diffusion idea used in CGI and proposed a regional gradient estimation strategy to improve the problem of gradient calculation in the CGI method. Next, the gradient value was used to determine whether a pixel was an edge pixel. Then, a 1D directional filter was employed to process edge pixels while interpolating non-edge pixels using a 2D directionless filter. Finally, we experimented with various representative interpolation methods for grayscale and color images, including the one presented in this paper, and compared them in terms of subjective results, objective criteria, and computational complexity. The experimental results showed that GEI performed better than the other methods in an experiment concerning the visual effect, objective criteria, and computational complexity. [ABSTRACT FROM AUTHOR]

A super-resolution generation model for cultural relics image(CR-SRGAN) was proposed in order to solve the problems caused by the long history, such as the dark and old surface of cultural relics and the fading of images. Aiming at the problem of image degradation, the model obtained the low resolution image data set corresponding to the high-resolution image by adding noise and color aging processing on the basis of the original bicubic interpolation down sampling, and then used the obtained high-resolution and low-resolution images to train generative adversarial network. The two sub networks continuously played games to optimize their own performance, and finally realized the color restoration of the dark and old cultural relic image and super-resolution image generation. The experimental results show that, compared with bicubic interpolation, CR-SRGAN has an average increase of 0.86 dB in peak signal to noise ratio (PSNR) and an average increase of 0.04 in structural similarity (SSIM). In addition, subjectively, the color of the faded image is also repaired when the texture is reconstructed.

A problem of single image super-resolution is considered, where the goal is to recover one high-resolution image from one low-resolution image. Whereas this problem has been successfully solved so far by the known VDSR network, such an approach still cannot give an overall beneficial effect compared to bicubic interpolation. This is so due to the fact that the image reconstruction quality has been estimated separately by three subjective factors. Moreover, the original VDSR network consisting of 20 convolutional layers is apparently not optimal by its depth. This is why here those factors are aggregated, and the network performance is deemed by a single estimator. Then the depth is tried to be decreased (truncation) along with adjusting the learning rate drop factor. Finally, a plausible improvement of the VDSR network is confirmed. The best truncated network, performing by almost 3.2 % better than bicubic interpolation, occupies less memory space and is about 1.44 times faster than the original VDSR network for images of a medium size.

This paper proposes an image interpolation method with regional gradient estimation (GEI) to solve the problem of the nonlinear interpolation method not sufficiently considering non-edge pixels. First, the approach presented in this paper expanded on the edge diffusion idea used in CGI and proposed a regional gradient estimation strategy to improve the problem of gradient calculation in the CGI method. Next, the gradient value was used to determine whether a pixel was an edge pixel. Then, a 1D directional filter was employed to process edge pixels while interpolating non-edge pixels using a 2D directionless filter. Finally, we experimented with various representative interpolation methods for grayscale and color images, including the one presented in this paper, and compared them in terms of subjective results, objective criteria, and computational complexity. The experimental results showed that GEI performed better than the other methods in an experiment concerning the visual effect, objective criteria, and computational complexity.

A super-resolution generation model for cultural relics image(CR-SRGAN)was proposed in order to solve the problems caused by the long history,such as the dark and old surface of cultural relics and the fading of images.Aiming at the problem of image degradation,the model obtained the low resolution image data set corresponding to the high-resolution image by adding noise and color aging processing on the basis of the original bicubic interpolation down sampling,and then used the obtained high-resolution and low-resolution images to train generative adversarial network.The two sub networks continuously played games to optimize their own performance,and finally realized the color restoration of the dark and old cultural relic image and super-resolution image generation.The experimental results show that,compared with bicubic interpolation,CR-SRGAN has an average increase of 0.86 dB in peak signal to noise ratio(PSNR)and an average increase of 0.04 in structural similarity(SSIM).In addition,subjectively,the color of the faded image is also repaired when the texture is reconstructed. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have recently made impressive results for image super-resolution (SR). Our goal is to introduce a new image SR framework rely on a CNN. In this paper, the input image is decomposed into luminance channel and chromatic channels. A designed network based on a residual dense network is introduced to extract the hierarchical features from luminance part. The bicubic interpolation is simply used to upscale low resolution (LR) chromatic channels. However, this step degrades the chromatic channels. To tackle this issue, the SR reconstructed luminance channel is applied as the reference image in guided filters to promote the interpolated chromatic channels. Guided filters technique has ability to retain sharp edges and fine details from the reference image and carry them to the target images. Extensive experiments on several commonly used image SR testing datasets demonstrate that our framework has the ability to extract features and outperforms existing well-known techniques for image SR by LR image into the high resolution (HR) image efficiently. [ABSTRACT FROM AUTHOR]

Bicubic interpolation is widely used in real-time image processing systems because of its quality. The real-time implementation of bicubic interpolation requires a lot of hardware resources, especially the number of multipliers because it represents high computational complexity. In this article, a set of algorithms that approximate the bicubic interpolation and reduce the hardware resource consumption are proposed. The proposed algorithms are based on combining linear and cubic interpolations. These algorithms are surveyed and compared in terms of interpolation quality, number of adders, number of multipliers, adaptive logic modules, lookup tables (LUTs), registers, and maximum operating frequency. These algorithms are implemented and tested on an Intel Cyclone V target. This article provides various choices of interpolation algorithms to cater to different application requirements, including accuracy, hardware resource consumption, and throughput performance. The implementation codes are available at github.com/DreamIP/Interpolation. [ABSTRACT FROM AUTHOR]

The article is devoted to the problem of efficient scaling of raster images. We consider some negative effects, related with scaling of raster images. Besides, we consider an analysis of several methods that are used to increase sizes of ras-ter images. Among them are nearest neighbor algorithm, bilinear interpolation, bicubic interpolation. We consider our research methodology, and then we tell about result of algorithms comparison. We use two criteria: quality of output images and performance of algorithms. Due to this research we can tell some recommendations on the choice of algo-rithms for increment of raster images. It is useful because there is no single universal algorithm for efficient solution to the problem.

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Copyright of Ingeniería Solidaria is the property of Universidad Cooperativa de Colombia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Highlights • To the best of our knowledge, the problem of resolution enhancement of the retinal images has not been addressed. • The proposed method takes into consideration the diagnostic information in the retinal fundus image during super-resolution (SR). • The method performs SR only on the zone of interest rather than the entire image. This leads to computational time efficiency. • A set of novel fundus image specific features are extracted to classify the diagnostically significant and non-significant zones. • A trade-off between the learning based method and bicubic interpolation is performed during SR which leads to achievement in computational time without loss in reconstruction accuracy. Abstract Advancements in tele-medicine have led to the development of portable and cheap hand-held retinal imaging devices. However, the images obtained from these devices have low resolution (LR) and poor quality that may not be suitable for retinal disease diagnosis. Therefore, this paper proposes a novel framework for the super-resolution (SR) of the LR fundus images. The method takes into consideration the diagnostic information in the fundus images during the SR process. In this work, SR is performed on the zone of interest of the fundus images. Clinical information of the selected zone is captured using the Shannon entropy, the contrast sensitivity index (CSI), the multi-resolution (MR) intra-band energy and the MR inter-band eigen features. The support vector machine (SVM) classifier is used to decide the clinical significance of the zone. Highly accurate learning based SR method or the bicubic interpolation is applied to the selected zone based on the classification output. The method is tested on the Standard Diabetic Retinopathy Database Calibration level 1 (DIARETDB1) and the Digital Retinal Images for Vessel Extraction (DRIVE) databases. Classification accuracy of 85.22% and 85.77% is achieved for the DIARETDB1 and DRIVE databases respectively. The SR performance of the algorithm is quantified in terms of peak signal to noise ratio (PSNR), structural similarity index measure (SSIM) and computational time. The proposed diagnostic information based SR achieves computational time efficiency without compromising with the high resolution (HR) reconstruction accuracy of the fundus image zones. [ABSTRACT FROM AUTHOR]

Global look-up table strategy proposed recently has been proven to be an efficient method to accelerate the interpolation, which is the most time-consuming part in the iterative sub-pixel digital image correlation (DIC) algorithms. In this paper, a global look-up table strategy with cubic B-spline interpolation is developed for the DIC method based on the inverse compositional Gauss–Newton (IC-GN) algorithm. The performance of this strategy, including accuracy, precision, and computation efficiency, is evaluated through a theoretical and experimental study, using the one with widely employed bicubic interpolation as a benchmark. The global look-up table strategy with cubic B-spline interpolation improves significantly the accuracy of the IC-GN algorithm-based DIC method compared with the one using the bicubic interpolation, at a trivial price of computation efficiency.