Superresolution in incoherent imaging system

Wadaka, Shusou; Sato, Takuso

doi:10.1364/josa.65.000354

Cited by 10 publications

(3 citation statements)

References 6 publications

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“…However, the work on superresolution, i.e., resolution beyond the diffraction limit, has shown that this is not the case. [36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51] Generally, the line of reasoning is as follows. Knowledge of the system's transfer function makes it possible to reconstruct the object spectrum within the passband of the imaging system by means of inverse filtering of the image spectrum.…”

Section: Resolution In the Frequency Domain: Extrapolation And Superrmentioning

confidence: 99%

Resolution: a survey

1997

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Section: Resolution In the Frequency Domain: Extrapolation And Superrmentioning

confidence: 99%

Resolution: a survey

1997

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“…Subsequently, Kim et al [Kim and Su (1993)] extended the observation model and Gerchberg [Gerchberg (1974)] presented a SR method for continuous energy decline setting. Wadaka et al [Wadaka and Sato (1975)] proposed a superimposed sinusoidal template method. Nevertheless, the abovementioned methods were used to obtain HR images from a single image in the frequency domain and the effects were not satisfactory.…”

Section: Introductionmentioning

confidence: 99%

Super-Resolution Reconstruction of Images Based on Microarray Camera

Zou¹,

Li²,

Zhi-jun³

et al. 2019

Computers, Materials &Amp; Continua

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In the field of images and imaging, super-resolution (SR) reconstruction of images is a technique that converts one or more low-resolution (LR) images into a highresolution (HR) image. The classical two types of SR methods are mainly based on applying a single image or multiple images captured by a single camera. Microarray camera has the characteristics of small size, multi views, and the possibility of applying to portable devices. It has become a research hotspot in image processing. In this paper, we propose a SR reconstruction of images based on a microarray camera for sharpening and registration processing of array images. The array images are interpolated to obtain a HR image initially followed by a convolution neural network (CNN) procedure for enhancement. The convolution layers of our convolution neural network are 3 3× or 1 1× layers, of which the 1 1× layers are used to improve the network performance particularly. A bottleneck structure is applied to reduce the parameter numbers of the nonlinear mapping and to improve the nonlinear capability of the whole network. Finally, we use a 3 3× deconvolution layer to significantly reduce the number of parameters compared to the deconvolution layer of FSRCNN-s. The experiments show that the proposed method can not only ameliorate effectively the texture quality of the target image based on the array images information, but also further enhance the quality of the initial high resolution image by the improved CNN.

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“…22 The concept and limits of the spectral far-field techniques ͑which will be exploited in this study͒ have been examined 17,23 and a superresolution algorithm developed for astronomical imagery at the University of California-San Diego has resulted in the founding of a private company ͑Pixon, Setauket, NY͒ dedicated to superresolution image enhancement. A significant amount of earlier work was performed in the areas of holographic superresolution by Sato, 24 including studies in ultrasound. However, this work was limited to reconstruction through correspondence techniques 25 or as an image-processing technique.…”

Section: Introductionmentioning

confidence: 99%

Superresolution ultrasound imaging using back-projected reconstruction

Clement

Huttunen

Hynynen

2005

The Journal of the Acoustical Society of America

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An ultrasound technique for imaging objects significantly smaller than the source wavelength is investigated. Signals from a focused beam are recorded over an image plane in the acoustic farfield and backprojected in the wave-vector domain to the focal plane. A superresolution image recovery method is then used to analyze the Fourier spatial frequency spectrum of the signal in an attempt to deduce the location and size of objects in this plane. The physical foundation for the method is rooted in the fact that high spatial frequencies introduced by the object in fact affect the lower (nonevanescent) spatial frequencies of the overall signal. The technique achieves this by using a priori measurements of the ultrasound focus in water, which gives full spectral information about the image source. A guess is then made regarding the size and location of the object that distorted the field, and this is convolved with the a priori measurement, thus creating a candidate image. A large number of candidates are generated and the one whose spectrum best matches the uncorrected image is accepted. The method is demonstrated using 0.34- and 0.60-mm wires with a focused 1.05-MHz ultrasound signal and then a human hair (approximately 0.03 mm) with a 4.7-MHz signal.

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Superresolution in incoherent imaging system

Cited by 10 publications

References 6 publications

Resolution: a survey

Resolution: a survey

Super-Resolution Reconstruction of Images Based on Microarray Camera

Superresolution ultrasound imaging using back-projected reconstruction

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