A Single Image Interpolation Scheme for Enhanced Super Resolution in Bio-Medical Imaging

Akhtar, Pervez; Azhar, Faisal

doi:10.1109/icbbe.2010.5518164

Cited by 13 publications

(6 citation statements)

References 7 publications

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“…Since edges are visually attractive to the human perceptual system, many algorithms focus on edge-directed interpolation methods [10,11]. The main idea of these methods is to preserve the edge sharpness during the image interpolation process.…”

Section: Edge-directed Interpolationmentioning

confidence: 99%

Image Interpolation via Scanning Line Algorithm and Discontinuous B-Spline

Liu

Wang

Pang

et al. 2017

MCA

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Image interpolation is a basic operation in image processing. Lots of methods have been proposed, including convolution-based methods, edge modeling methods, point spread function (PSF)-based methods or learning-based methods. Most of them, however, present a high computational complexity and are not suitable for real time applications. However, fast methods are not able to provide artifacts-free images. In this paper we describe a new image interpolation method by using scanning line algorithm which can generate C −1 curves or surfaces. The C −1 interpolation can truncate the interpolation curve at big skipping; hence, the image edge can be kept. Numerical experiments illustrate the efficiency of the novel method.

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Section: Edge-directed Interpolationmentioning

confidence: 99%

Image Interpolation via Scanning Line Algorithm and Discontinuous B-Spline

Liu

Wang

Pang

et al. 2017

MCA

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“…The interpolated surface is smoother than corresponding surfaces obtained by bilinear interpolation or nearest-neighbor interpolation. Bicubic interpolation [10] can be accomplished using either Lagrange polynomials, cubic splines, or cubic convolution algorithm. In image processing, bicubic interpolation is often chosen over bilinear interpolation or nearest neighbor in image resampling, when speed is not an issue.…”

Section: 7) Bicubic Interpolationmentioning

confidence: 99%

Enhancement of Color Images by Scaling the DCT Coefficients and Interpolation

Alias¹,

George²

2012

IJCA

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This paper presents the enhancement of color images in the frequency domain with interpolation. The novelty in our approach is the treatment of the chromatic components, while previous techniques treated only the luminance components and the enhanced image is interpolated with bicubic interpolation. After interpolation, the interpolated surface is smoother than corresponding surface obtained by the enhancement. The treatment of chromatic components improves the visual quality of the images to a great extent. The proposed technique, is more efficient than the spatial domain method. This paper investigate how we can increase the number of pixels and there by smoothening the enhanced surface.

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“…21,27,30 Despite advances in generative models, limitations persist in achieving synthetic biological images that look realistic, as assessed by rigorous metrics. [21][22][23][24][25][26][27][28][31][32][33] Issues such as the accurate representation of subtle or rare textures, cell arrangements, and tissue boundaries are areas of active research. 22,26 Here, we explore interpolation techniques, such as frame interpolation for large motion (FILM), to enhance the resolution of 3D biological images.…”

Section: Introductionmentioning

confidence: 99%

Generative interpolation and restoration of images using deep learning for improved 3D tissue mapping

Joshi,

Forjaz,

Sang Han

et al. 2024

Preprint

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The development of novel imaging platforms has improved our ability to collect and analyze large three-dimensional (3D) biological imaging datasets. Advances in computing have led to an ability to extract complex spatial information from these data, such as the composition, morphology, and interactions of multi-cellular structures, rare events, and integration of multi-modal features combining anatomical, molecular, and transcriptomic (among other) information. Yet, the accuracy of these quantitative results is intrinsically limited by the quality of the input images, which can contain missing or damaged regions, or can be of poor resolution due to mechanical, temporal, or financial constraints. In applications ranging from intact imaging (e.g. light-sheet microscopy and magnetic resonance imaging) to sectioning based platforms (e.g. serial histology and serial cryo-electron microscopy), the quality and resolution of imaging data has become paramount. Here, we address these challenges by leveraging frame interpolation for large image motion (FILM), a generative AI model originally developed for temporal interpolation, for spatial interpolation of a range of 3D image types. Comparative analysis demonstrates the superiority of FILM over traditional linear interpolation to produce functional synthetic images, due to its ability to better preserve biological information including microanatomical features and cell counts, as well as image quality, such as contrast, variance, and luminance. FILM repairs tissue damages in images and reduces stitching artifacts. We show that FILM can decrease imaging time by synthesizing skipped images. We demonstrate the versatility of our method with a wide range of imaging modalities (histology, tissue-clearing/light-sheet microscopy, magnetic resonance imaging, cryo-electron microscopy), species (human, mouse), healthy and diseased tissues (pancreas, lung, brain), staining techniques (IHC, H&E), and pixel resolutions (8 nm, 2 μm, 1mm). Overall, we demonstrate the marked potential of generative AI in improving the resolution, throughput, and quality of biological image datasets, enabling improved 3D imaging.

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A Single Image Interpolation Scheme for Enhanced Super Resolution in Bio-Medical Imaging

Cited by 13 publications

References 7 publications

Image Interpolation via Scanning Line Algorithm and Discontinuous B-Spline

Image Interpolation via Scanning Line Algorithm and Discontinuous B-Spline

Enhancement of Color Images by Scaling the DCT Coefficients and Interpolation

Generative interpolation and restoration of images using deep learning for improved 3D tissue mapping

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