An efficiently phase-shift frequency domain method for super-resolution image processing

Bui-Thu, Cao; Do-Hong, Tuan; Le-Tien, Thuong; Nguyen-Duc, Hoang

doi:10.1109/atc.2009.5349335

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…Subscripts i and i+1 refer to two consecutive focal planes. According to the [37], the images of in-focus objects tend to have more high frequency components and higher power in the frequency domain. The patches in this paper are around the candidate points which are selected by MIDN, and most of the pixels in the patches are near the focal planes.…”

Section: Depth Definition By Frequency Modelmentioning

confidence: 99%

Convolutional Neural Network for Extracting 3D Point Clouds of Fibrous Web From Multi-Focus Images

Hou

Ouyang

et al. 2020

IEEE Access

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This paper presents a new method for extracting 3D point clouds from multi-focus images of a fibrous web acquired on an optical microscope to analyze microscopic structures of a fibrous web. The algorithm consists of two major parts: (1) utilizing a convolutional neural network (CNN) to extract in-focus objects from multi-focus images, and (2) a depth identification module (DIM) which is a frequency domain-based model used to identify the depths of object points. The network, namely the multi-focus image deblurring network (MIDN), was designed by introducing gradient features into the network to deblur images and generate the ranges of focal depths of object points. Based on the results of MIDN, DIM was constructed to calculates the focal plane depth for each point. The experiments show that the combination of MIDN and DIM provides a practical way to generate complete, accurate 3D structures of nonwoven.

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Section: Depth Definition By Frequency Modelmentioning

confidence: 99%

Convolutional Neural Network for Extracting 3D Point Clouds of Fibrous Web From Multi-Focus Images

Hou

Ouyang

et al. 2020

IEEE Access

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“…With and in range of , and (3.16) has to satisfy four conditions, as follow: (9) From (8) and four conditions of (9), we can find out the parameters of and . Based on the parameters we can define the Pchip interpolation function of .…”

Section: Setmentioning

confidence: 99%

A robust combination interpolation method for video super-resolution

Bui

Hong

et al. 2013

Sci. Tech. Dev. J.

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This paper presents an efficient method for video super-resolution (SR) based on two main ideals: Firstly, input video frames can be separated into two components, nontexturing image and texturing image. Then each component image is applied to a compatible interpolation method to improve the quality of high-resolution (HR) reconstructed frame. Secondly, based on the approach that border regions of image details are the most lossy information regions from the sampling process. Therefore, a task of compensation interpolation is essential to increase the quality of the reconstructed HR images. From these discussions, we proposed an efficient method for video SR by combining the spatial interpolation in different texturing regions and the sampling compensation interpolation to improve the quality of video super-resolution. Our results shown that, the quality of HR frames, reconstructed by the proposed method, is better than that of other methods, , and in recently. The significant contribution is the low complexity of the proposed method. Hence, it is possible to apply the proposed algorithm to real-time video super-resolution applications.

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Scene-based video super-resolution with minimum mean square error estimation

Bui-Thu

Do-Hong

Le-Tien

et al. 2011

The 2011 International Conference on Advanced Technologies for Communications (ATC 2011)

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Motion estimation is a key problem in video superresolution (SR). If the estimation is highly accurate then the high resolution (HR) frames reconstructed is better in quality.Otherwise with small errors in estimation, they will create more degradation in the reconstructed HR frames. In many recent studies, the motion estimation is applied on every block of pixels. There is too little input data for estimating process so that it is hard to get high accuracy in results. This paper presents a new method for SR video image reconstruction through two main ideas. First, video frames are separated into two sections, as scene and motive objects. The motions of the scene are the same and uniform. We will have much data for estimating, so that the result can be more accurate. Second, the motion estimation is based on three parameters, rotation and shifts in vertical and horizontal. It presents a perfectly estimating for real motion of camera when capturing video frames. Based on that, an efficient algorithm is proposed by combination of block matching search method and minimum mean square error estimation. The results of the proposed algorithm are more accurate than those of other recent algorithms. It can be easy to see one we visualize the HR video frames reconstructed by other algorithms.

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An efficiently phase-shift frequency domain method for super-resolution image processing

Cited by 6 publications

References 11 publications

Convolutional Neural Network for Extracting 3D Point Clouds of Fibrous Web From Multi-Focus Images

Convolutional Neural Network for Extracting 3D Point Clouds of Fibrous Web From Multi-Focus Images

A robust combination interpolation method for video super-resolution

Scene-based video super-resolution with minimum mean square error estimation

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