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2010, Applied optics
In optical imaging, the resolution of the imaging system is not only limited by the aperture and imperfec-tion of the lens, but also by the CCD nonzero pixel size and separation between the two consecutive pixels. We deal only with the geometric superresolution and assume that ...
Optics Communications, 2004
We present a method to achieve superresolution in high numerical aperture systems. The idea is based on an extension of a pupil mask where not only the amplitude and phase of the field but also the polarisation are modulated. By spatially varying the polarisation, phase and amplitude distribution of the field in the pupil we can optimise the field distribution in the focal region in order to obtain a decrease in the lateral spot-size. Polarisation adds a new degree of freedom and leads to interesting results as shown in this paper.
Proceedings of SPIE, 2006
Theoretical and practical limitations usually constrain the achievable resolution of any imaging device. Super-Resolution (SR) methods are developed through the years to go beyond this limit by acquiring and fusing several low-resolution (LR) images of the same scene, producing a high-resolution (HR) image. The early works on SR, although occasionally mathematically optimal for particular models of data and noise, produced poor results when applied to real images. In this paper, we discuss two of the main issues ...
Journal of the Optical Society of America A, 2009
The resolution of every imaging system is limited either by the F-number of its optics or by the geometry of its detection array. The geometrical limitation is caused by lack of spatial sampling points as well as by the shape of every sampling pixel that generates spectral low-pass filtering. We present a novel approach to overcome the low-pass filtering that is due to the shape of the sampling pixels. The approach combines special algorithms together with spatial masking placed in the intermediate image plane and eventually allows geometrical superresolved imaging without relation to the actual shape of the pixels.
Applied Optics, 1992
The Lukosz technique of superresolution by spatial and temporal frequency interaction is extended. The effects of various misalignments and other errors are considered. An implementation of the technique is presented. Experimental results are given.
Optics Letters, 1991
The confocal optical system",2 is essentially a coher-ent optical processor for which the transfer function is given by the convolution of the pupil functions of the two imaging lenses. In the usual case of two equal, circular, unaberrated lenses, this is a well-behaved, smooth, ...
Optics Express, 2006
In this paper we describe a super-resolving approach based upon gray level coding of the information. Thus, the imaged object should have limited number of gray levels. The proposed approach overcomes the resolution limitations caused either by the optics or by the finite size of the detector. In contrast to other existing super resolution techniques that use time or wavelength multiplexing, in this approach one does not need to pay neither in temporal nor in wavelength degrees of freedom, but in intensity dynamic range. After the gray coding and the imaging, the high frequency spatial resolution features are decoded using the decoding gray level lookup table.
2010
The subject of extracting particular high-resolution data from low-resolution images is one of the most important digital image processing applications in recent years, attracting much research. This paper shows how to improve the resolution of real images when given image is in the degraded form. In the superresolution restoration problem, an improved resolution image is restored from several geometrically warped, blurred, and noisy and downsampled measured images. To obtain this result the use an iterative nonlinear restoration blind deconvolution maximum likely-hood algorithm imposing the low frequencies complete data of the original low-resolution image and the high-resolution data present only in a fraction of the image which suppresses the noise amplification and avoid the ringing in deblurred image. Our results show that a high resolution real image derived from superresolution methods enhance spatial resolution and provides substantially more image details.
2009
The superresolution algorithms typically transform the images into 1D vectors and perform operations on these vectors to obtain a high resolution image. Transforming the images into vectors results in computations with large matrices. In this paper, we first propose a 2D model for superresolution that treats the images as matrices and hence reduces the computational complexity. In the second part of the paper, we apply this model to superresolution of face images. Zhang et al.
Europhysics Letters (EPL), 1989
One of the basic properties of confocal scanning laser microscopy (CSLM) is the possibility of high axial resolution in the case of fluorescent object% As a consequence 3D images of 30 objects can be obtained. In the previous papers of this series it has been demonstrated that an improvement of lateral resolution in CSLM (lateral super-resolution) can be obtained if, at each step of the scanning procedure, the full image is detected in the image plane and these data are inverted to estimate the object at the confocal point. In this paper we prove that the same data contain sufficient information for improving also the axial resolution of CSLM (axial superresolution). The analysis is performed in the c u e of a simplified model i.e. two-dimensional objects (one lateral plus one axial dimension) and lenses with a small numerical apenure.
SPIE Newsroom, 2007
A new unifying approach to problems common to multiple degraded low-resolution images makes it possible to correct imperfections and to discover unseen details.
Frontiers in Optics 2009/Laser Science XXV/Fall 2009 OSA Optics & Photonics Technical Digest, 2009
We demonstrate working superresolution with Plenoptic 2.0 camera without need for traditional image registration in software. This paper describes our method, based only on the camera geometry and microlens parameters.
Journal of the Optical Society of America A, 2011
In this paper, we address the geometrical resolution limitation of an imaging sensor caused by the size of its pixels yielding insufficient spatial sampling of the image. The spatial blurring that is caused due to inadequate sampling can be resolved by placing a two-dimensional binary random mask in an intermediate image plane and shifting it along one direction while keeping the sensor as well as all other optical components fixed. Out of the set of images that are captured, a high resolution image can be decoded. In addition, this approach allows improved robustness to spatial noise.
Applied Optics, 2001
Objects that temporally vary slowly can be superresolved by the use of two synchronized moving masks such as pinholes or gratings. This approach to superresolution allows one to exceed Abbe's limit of resolution. Moreover, under coherent illumination, superresolution requires a certain approximation based on the time averaging of intensity rather than of field distribution. When extensive digital postprocessing can be incorporated into the optical system, a detector array and some postprocessing algorithms can replace the grating that is responsible for information decoding. In this way, no approximation is needed and the synchronization that is necessary when two gratings are used is simplified. Furthermore, we present two novel approaches for overcoming distortions when extensive digital postprocessing cannot be incorporated into the optical system. In the first approach, one of the gratings, in the input or at the output plane, is shifted at half the velocity of the other. In the second approach, various spectral regions are transmitted through the system's aperture to facilitate postprocessing. Experimental results are provided to demonstrate the properties of the proposed methods.
The paper presents super resolving configurations that are integrating two digital mirror devices (DMDs) in the aperture and/or in the intermediate image plane. The usage of the DMDs allows obtaining geometric resolution improvement, enhancing field of view and reduction of aberrations such as defocusing and blurring that is obtained due to relative movement during the integration time. The idea behind all the above mentioned applications is to use the DMDs to properly encode the space and the spatial frequency domains such that the object's information can be separated from the above mentioned aberrations, distortions, limitations and noises.
Advanced Signal Processing Algorithms, Architectures, and Implementations Xviii, 2008
Imaging plays a key role in many diverse areas of application, such as astronomy, remote sensing, microscopy, and tomography. Owing to imperfections of measuring devices (e.g., optical degradations, limited size of sensors) and instability of the observed scene (e.g., object motion, media turbulence), acquired images can be indistinct, noisy, and may exhibit insufficient spatial and temporal resolution. In particular, several external effects blur images. Techniques for recovering the original image include blind deconvolution (to remove blur) and superresolution (SR). The stability of these methods depends on having more than one image of the same frame. Differences between images are necessary to provide new information, but they can be almost unperceivable. State-of-the-art SR techniques achieve remarkable results in resolution enhancement by estimating the subpixel shifts between images, but they lack any apparatus for calculating the blurs. In this paper, after introducing a review of current SR techniques we describe two recently developed SR methods by the authors. First, we introduce a variational method that minimizes a regularized energy function with respect to the high resolution image and blurs. In this way we establish a unifying way to simultaneously estimate the blurs and the high resolution image. By estimating blurs we automatically estimate shifts with subpixel accuracy, which is inherent for good SR performance. Second, an innovative learning-based algorithm using a neural architecture for SR is described. Comparative experiments on real data illustrate the robustness and utilization of both methods.
Image Processing: Algorithms and Systems IV, 2005
This paper presents a method to predict the limit of possible resolution enhancement given a sequence of lowresolution images. Three important parameters influence the outcome of this limit: the total Point Spread Function (PSF), the Signal-to-Noise Ratio (SNR) and the number of input images. Although a large number of input images captured by a system with a narrow PSF and a high SNR are desirable, these conditions are often not achievable simultaneously. To improve the SNR, cameras are designed with near optimal quantum efficiency and maximum fill-factor. However, the latter widens the system PSF, which puts more weight on the deblurring part of a super-resolution (SR) reconstruction algorithm. This paper analyzes the contribution of each input parameters to the SR reconstruction and predicts the best attainable SR factor for given a camera setting. The predicted SR factor agrees well with an edge sharpness measure computed from the reconstructed SR images. A sufficient number of randomly positioned input images to achieve this limit for a given scene can also be derived assuming Gaussian noise and registration errors.
Optics Communications, 2014
In this paper, we generalize the method of using a 2-D moving binary random mask to overcome the geometrical resolution limitation of an imaging sensor. The spatial blurring is caused by the size of the imaging sensor pixels which yield insufficient spatial sampling. The mask is placed in an intermediate image plane and can be shifted in any direction while keeping the sensor as well as all other optical components fixed. Out of the set of images that are captured and registered, a high resolution image can be composed. In addition, this proposed approach reduces the amount of required computations and it has an improved robustness to spatial noise.
ijcset.com
I. INTRODUCTION Super-resolution image restoration refers to the image processing algorithm which produces high quality, superresolution (SR) images from a set of low-quality, low resolution (LR) images. It is generally regarded as consisting of three steps image registration, ...
Journal of the Optical Society of America A, 1986
The limitations of superresolving filters in imaging systems are investigated. The constraints on such filters in the nonscanning imaging mode are discussed. The possible advantages of such filters in confocal scanning imaging are highlighted. It is shown theoretically and verified experimentally that simply designed complex-amplitude filters can be used effectively to double the exit pupil of a confocal imaging system and thus improve resolution. Superresolution can be achieved with acceptable energy losses and manufacturing tolerances.
18th International Conference on Pattern Recognition (ICPR'06), 2006
Image/video processing including image enhancement, super resolution, inpainting, and video deinterlacing Computer vision including vision-based detection and tracking, image search, and 3D geometry Machine learning and pattern recognition
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