Topic
Image scaling
About: Image scaling is a research topic. Over the lifetime, 3541 publications have been published within this topic receiving 50108 citations. The topic is also known as: upscaling & downscaling.
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01 Jul 2019
TL;DR: This paper takes total variational and Morphological Component Analysis (MCA) techniques to reduce noise and interpolate missing data for reconstruction of retinal optical coherence tomography (OCT) images and results show that the (DCT+Curvelet) combination preserve the texture of the image well and the (B-scans-DWT-Curvelets) combination has better performance in structure preservation.
Abstract: In this paper, we apply combination of sparse representations and a total variation for reconstruction of retinal optical coherence tomography (OCT) images. The OCT imaging is based on interferometry, therefore OCT images suffer from the existence of a high level of noise. Utilization of effective interpolation and denoising algorithms are necessary to reconstruct high-resolution OCT images, especially when the subsampling of data is done during acquisition. In this paper, we take total variational and Morphological Component Analysis (MCA) techniques to reduce noise and interpolate missing data. Different over-complete dictionaries are constructed by using curvelet transform, wavelet transform or DCT, which represent the texture and cartoon layers in B-scans. Comparative analysis of image interpolation is done by two combinations of dictionaries, which are (DCT+Curvelet) and (DWT+Curvelet) transforms. Layered structures are more distinguished in reconstructed image with curvelet dictionary and textures are mostly detectable by wavelet or DCT. Evaluations are done both visually and in terms of different performance measures. Our simulation results show that the (DCT+Curvelet) combination preserve the texture of the image well and the (DWT+Curvelet) combination has better performance in structure preservation.
06 Sep 2012
TL;DR: The results showed that the digital integral interpolator software system was developed using LabVIEW can simulate the specific interpolation process of the interpolation algorithm visually and intuitively, which can be used to assist teaching.
Abstract: In order to facilitate the students to visually understand digital integral interpolation principle and interpolation process, digital integral interpolator software system was developed using LabVIEW. The system is integrated with linear interpolation and arc interpolation modules, and every module mainly included the interpolation arithmetic initialization start function, interpolation speed control function and interpolation process real -time display function. The front and back panels of each module were designed based on LabVIEW platform, the programming flow charts were designed, and the system was tested comprehensively; the results showed that the interpolator can not only realize single quadrant linear and circular interpolation, but also can realize the span quadrant linear and circular interpolation; furthermore, it can simulate the specific interpolation process of the interpolation algorithm visually and intuitively, which can be used to assist teaching. Introduction
TL;DR: In this article , the authors proposed quantum bilinear interpolation algorithms based on geometric centers using fault-tolerant implementations of quantum arithmetic operators, which can keep the consistency between the geometric centers of the original and target images.
Abstract: Bilinear interpolation is widely used in classical signal and image processing. Quantum algorithms have been designed for efficiently realizing bilinear interpolation. However, these quantum algorithms have limitations in circuit width and garbage outputs, which block the quantum algorithms applied to noisy intermediate-scale quantum devices. In addition, the existing quantum bilinear interpolation algorithms cannot keep the consistency between the geometric centers of the original and target images. To save the above questions, we propose quantum bilinear interpolation algorithms based on geometric centers using fault-tolerant implementations of quantum arithmetic operators. Proposed algorithms include the scaling-up and scaling-down for signals (grayscale images) and signals with three channels (color images). Simulation results demonstrate that the proposed bilinear interpolation algorithms obtain the same results as their classical counterparts with an exponential speedup. Performance analysis reveals that the proposed bilinear interpolation algorithms keep the consistency of geometric centers and significantly reduce circuit width and garbage outputs compared to the existing works.
TL;DR: In this article , a flow-based video frame interpolation (VFI) method is proposed to synthesize new frames in between the original frames in a video by estimating the motion of pixels.
Abstract: Flow-based video frame interpolation (VFI) is the process of synthesizing new frames in between the original frames in a video by estimating the motion of pixels. In this paper, usage of flow-based VFI methods has been proposed on 3D MRI images in order to convert low-resolution (LR) images into their high-resolution (HR) counterparts. This paper demonstrates the process of increasing the spatial resolution of the 3D image successively along all three axes to obtain a super-resolution (SR) 3D image using VFI. Quantitative analysis of the results proposes that this method of 3D spatial enhancement outperforms the previously proposed methods in the majority of the circumstances.Keywords3D imagesSuper-resolutionFrame interpolationOptical flow