Using High Boost Bi-cubic Interpolation to Upscale and Enhance the Medical Image Details

Thanakitivirul, Parawata; Khetkeeree, Suphongsa; Charnsamorn, Chapkit

doi:10.1109/ecti-con49241.2020.9158079

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…In order to obtain the unknown data from its related known data, interpolation is an important method. It has been widely applied in several applications, i.e., signal reconstruction [1]- [9], signal and image denoising [10]- [12], medical image processing [13]- [18], image resizing [19]- [28], image classification [29], image compression [30], image enhancement [31], change detection [32], video processing [33], and more [34], [35]. The interpolation is used to generate the new data points from the discrete set of known data points in case of its position is within the range of known data.…”

Section: Introductionmentioning

confidence: 99%

Symmetric quadratic tetration interpolation using forward and backward operation combination

Charnsamorn

Khetkeeree

2022

IJECE

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The existed interpolation method, based on the second-order tetration polynomial, has the asymmetric property. The interpolation results, for each considering region, give individual characteristics. Although the interpolation performance has been better than the conventional methods, the symmetric property for signal interpolation is also necessary. In this paper, we propose the symmetric interpolation formulas derived from the second-order tetration polynomial. The combination of the forward and backward operations was employed to construct two types of the symmetric interpolation. Several resolutions of the fundamental signals were used to evaluate the signal reconstruction performance. The results show that the proposed interpolations can be used to reconstruct the fundamental signal and its peak signal to noise ratio (PSNR) is superior to the conventional interpolation methods, except the cubic spline interpolation for the sine wave signal. However, the visual results show that it has a small difference. Moreover, our proposed interpolations converge to the steady-state faster than the cubic spline interpolation. In addition, the option number increasing will reinforce their sensitivity.

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Section: Introductionmentioning

confidence: 99%

Symmetric quadratic tetration interpolation using forward and backward operation combination

Charnsamorn

Khetkeeree

2022

IJECE

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“…Considering the uneven distribution of image features, Duanmu et al [14] and Zhang et al [15], from the perspectives of Sobel operator and rational fractal interpolation, adopted distinct sampling methods for homogeneous regions and texture regions to overcome the blurring of edges. Furthermore, combined with a series of theories such as Discrete Wavelet Transform (DWT) [16], Whittaker filtering [17], partial Least squares method [18], Trilateral filter [19], high boost operator [20], etc., the interpolation method imbibed many aspects of optimization.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Feature Mapping Network for Hyperspectral Image Super-Resolution

et al. 2021

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Hyperspectral Image (HSI) can continuously cover tens or even hundreds of spectral segments for each spatial pixel. Limited by the cost and commercialization requirements of remote sensing satellites, HSIs often lose a lot of information due to insufficient image spatial resolution. For the high-dimensional nature of HSIs and the correlation between the spectra, the existing Super-Resolution (SR) methods for HSIs have the problems of excessive parameter amount and insufficient information complementarity between the spectra. This paper proposes a Multi-Scale Feature Mapping Network (MSFMNet) based on the cascaded residual learning to adaptively learn the prior information of HSIs. MSFMNet simplifies each part of the network into a few simple yet effective network modules. To learn the spatial-spectral characteristics among different spectral segments, a multi-scale feature generation and fusion Multi-Scale Feature Mapping Block (MSFMB) based on wavelet transform and spatial attention mechanism is designed in MSFMNet to learn the spectral features between different spectral segments. To effectively improve the multiplexing rate of multi-level spectral features, a Multi-Level Feature Fusion Block (MLFFB) is designed to fuse the multi-level spectral features. In the image reconstruction stage, an optimized sub-pixel convolution module is used for the up-sampling of different spectral segments. Through a large number of verifications on the three general hyperspectral datasets, the superiority of this method compared with the existing hyperspectral SR methods is proved. In subjective and objective experiments, its experimental performance is better than its competitors.

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