• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.1025-1030
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• 1997

In this study,the procedure of interpolation surface modeling on bicubic spline patch equation and NC machining are presented. The procedure consists of three parts : patch modeling,cutter location data generation,post processing and NC milling machining. For generation of the cutter location data,tangent vectors and units normal vectors on the patch must be calculated. In order to investigate the properties of the interpolation surface created by bicubic spline patch, two kinds of end conditions, clamped end condition and relaxed end condition,were applied in this study. The shape of the patch depends on the magnitide of the tangent vectors and twist vectors at the corners of bicubic surface patch. the patch generated by relaxed end condition more approximated to the surface patch which was given.

• Journal of the Korean Graphic Arts Communication Society
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• v.16 no.3
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• pp.81-95
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• 1998

If a image doesn't include sufficient data of output size and resolution, we will scan again the image. Interpolation generates a new pixel by methematical average of processing. In the interpolation method, there are nearest neighbor interpolation, bilinear interpolation and bicubic interpolation etc. This study was carried out for the purpose of researching compatible method to digital scan image caused by only different interpolation methods. Nearest neighbor interpolation show superior effect in the drawing image. Bilinear interpolation show reduction in detail and contrast. Bicubic interpolation show superior effect in the digital photo image USM(Unsharp Mask) application after extension by interpolation show better than extension by interpolation after USM(unsharp mask) application.

• Smart Media Journal
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• v.9 no.4
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• pp.17-25
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• 2020

In the image object classification problem, low-resolution images may have a negative impact on the classification result, especially when the classification method, such as a convolutional neural network (CNN) model, is trained on a high-resolution (HR) image dataset. In this paper, we analyze the behavior of applying a classical super-resolution (SR) method such as bicubic interpolation, and a deep CNN model such as SRCNN to enhance low-resolution (LR) weeds images used for classification. Using an HR dataset, we first train a CNN model for weeds image classification with a default input size of 128 × 128. Then, given an LR weeds image, we rescale to default input size by applying the bicubic interpolation or the SRCNN model. We analyze these two approaches on the Chonnam National University (CNU) weeds dataset and find that SRCNN is suitable for the image size is smaller than 80 × 80, while bicubic interpolation is convenient for a larger image.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.9
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• pp.2351-2369
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• 2012

This paper describes interpolation method of motion field in the Wyner-Ziv video coding (WZVC) based on Expectation-Maximization (EM) algorithm. In the EM algorithm, the estimated motion field distribution is calculated on a block-by-block basis. Each pixel in the block shares similar probability distribution, producing an undesired blocking artefact on the pixel-based motion field. The proposed interpolation techniques are Bicubic and Lanczos which successively use 16 and 32 neighborhood probability distributions of block-based motion field for one pixel in k-by-k block on pixel-based motion field. EM-based WZVC codec updates the estimated probability distribution on block-based motion field, and interpolates it to pixel resolution. This is required to generate higher-quality soft side information (SI) such that the decoding algorithm is able to make syndrome estimation more quickly. Our experiments showed that the proposed interpolation methods have the capability to reduce EM-based WZVC decoding complexity with small increment of bit rate.

• Journal of information and communication convergence engineering
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• v.21 no.1
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• pp.17-23
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• 2023

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.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.1
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• pp.152-159
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• 2010

Existing character recognition algorithm recognize characters in simple conditional. It has the disadvantage that recognition rates often drop drastically when input document image has low quality, rotated text, various font or size text because of external noise or data loss. In this paper, proposes the optical character recognition algorithm which using bicubic interpolation method for the catalog retrieval when the input image has rotated text, blurred, various font and size. In this paper, applied optical character recognition algorithm consist of detection and recognition part. Detection part applied roberts and hausdorff distance algorithm for correct detection the catalog of book. Recognition part applied bicubic interpolation to interpolate data loss due to low quality, various font and size text. By the next time, applied rotation for the bicubic interpolation result image to slant proofreading. Experimental results show that proposal method can effectively improve recognition rate 6% and search-time 1.077s process result.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.3
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• pp.112-118
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• 2014

As higher quality of image is required for digital image scaling, longer processing time is required. Therefore the technology that can make higher quality image quickly is needed. We propose the double linear-cubic convolution interpolation which creates the high quality image with low complexity and hardware resources. The proposed interpolation methods which are made up of four one-dimensional linear interpolations and one one-dimensional cubic convolution perform linear-cubic convolution interpolation in horizontal and vertical direction. When compared in aspects of peak signal-to-noise ratio(PSNR), performance time and amount of hardware resources, the proposed interpolation provided better PSNR, low complexity and less hardware resources than bicubic convolution interpolation.

• Journal of Multimedia Information System
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• v.8 no.2
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• pp.101-110
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• 2021

In computer vision, single-image super resolution has been an area of research for a significant period. Traditional techniques involve interpolation-based methods such as Nearest-neighbor, Bilinear, and Bicubic for image restoration. Although implementations of convolutional neural networks have provided outstanding results in recent years, efficiency and single model multi-scalability have been its challenges. Furthermore, previous works haven't placed enough emphasis on real-number scalability. Interpolation-based techniques, however, have no limit in terms of scalability as they are able to upscale images to any desired size. In this paper, we propose a convolutional neural network possessing the advantages of the interpolation-based techniques, which is also efficient, deeming it suitable in practical implementations. It consists of convolutional layers applied on the low-resolution space, post-up-sampling along the end hidden layers, and additional layers on high-resolution space. Up-sampling is applied on a multiple channeled feature map via bicubic interpolation using a single model. Experiments on architectural structure, layer reduction, and real-number scale training are executed with results proving efficient amongst multi-scale learning (including scale multi-path-learning) based models.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.12C
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• pp.1184-1193
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• 2006

Image interpolation has been widely used and studied in the various fields of image processing. There are many approaches of varying complexity and robustness. In this paper, a new distance weight is proposed for the conventional linear interpolation. In comparison with the conventional linear weight, the new distance weight uses a quadratic or cubic polynomial equation to reflect that the interpolated value should be influenced more by the value of closer pixels in an input image. In this paper, the new adaptive linear (NAL) interpolation, which considers patterns near the interpolated value, is also proposed. This algorithm requires a pattern weight, which is used to determine the ratio of reflection on local patterns, to obtain an interpolated image that exhibits better quality at various magnification factors (MF). In the computer simulation, not only did the NAL interpolation exhibit much lower computational complexity than conventional bicubic interpolation, it also improved peak signal-to-noise ratios (PSNR).

• Journal of IKEEE
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• v.27 no.3
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• pp.296-302
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• 2023

As Multimedia industry has evolved, it has become possible to display resolutions in various formats. Therefore, the performance of a scaler algorithm that converts resolutions while maintaining high quality and its hardware implementation are important. Considering the hardware design of an image up/down scaler, this paper proposes a combinatorial scaler algorithm that uses modified bilinear interpolation in the vertical direction and bicubic interpolation in the horizontal direction to reduce the line memory burden. Through quantitative and qualitative evaluations, this paper compared the performance of the proposed algorithm with three other well-known algorithms, and also compared the hardware burden of its hardware implementation. This paper used a sinusoidal signal and eight typical images for performance evaluation.