Motion Detection and Image Restoration Based on Video Reconstruction from a Single Coded Exposure Photograph

Satellites have many high-, medium-, and low-frequency micro vibration sources that lead to the optical axis jitter of the optical load and subsequently degrade the remote sensing image quality. To address this problem, this paper developed an image motion detection and restoration method based on an inertial reference laser, and describe edits principle and key components. To verify the feasibility and performance of this method, this paper also built an image motion measurement and restoration system based on an inertial reference laser, which comprised a camera (including the inertial reference laser unit and a Hartmann wavefront sensor), an integrating sphere, a simulated image target, a parallel light pope, a vibration isolation platform, a vibration generator, and a 6 degrees of freedom platform. The image restoration principle was also described. The background noise in the experiment environment was measured, and an image motion measurement accuracy experiment was performed. Verification experiments of image restoration were also conducted under various working conditions. The experiment results showed that the error of image motion detection based on the inertial reference laser was less than 0.12 pixels (root mean square). By using image motion data to improve image quality, the modulation transfer function (MTF) of the restored image was increased to 1.61–1.88 times that of the original image MTF. The image motion data could be used as feedback to the fast steering mirror to compensate for the satellite jitter in real time and to directly obtain high-quality images.

Download Full-text

The optimal code searching method with an improved criterion of coded exposure for remote sensing image restoration

Optics Communications ◽

10.1016/j.optcom.2014.11.021 ◽

2015 ◽

Vol 338 ◽

pp. 540-550 ◽

Cited By ~ 7

Author(s):

Lirong He ◽

Guangmang Cui ◽

Huajun Feng ◽

Zhihai Xu ◽

Qi Li ◽

...

Keyword(s):

Remote Sensing ◽

Image Restoration ◽

Remote Sensing Image ◽

Optimal Code ◽

Searching Method ◽

Coded Exposure

Download Full-text

CodedRecon: Video reconstruction for coded exposure imaging techniques

Software Impacts ◽

10.1016/j.simpa.2021.100064 ◽

2021 ◽

pp. 100064

Author(s):

Prasan Shedligeri ◽

Anupama S. ◽

Kaushik Mitra

Keyword(s):

Imaging Techniques ◽

Video Reconstruction ◽

Coded Exposure

Download Full-text

Analysis of 3-d molecular distribution with the digital imaging microscope

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100102286 ◽

1988 ◽

Vol 46 ◽

pp. 40-41

Author(s):

W.A. Carrington ◽

F.S. Fay ◽

K.E. Fogarty ◽

L. Lifshitz

Keyword(s):

Image Restoration ◽

Digital Imaging ◽

Fluorescent Dyes ◽

Optical Axis ◽

Computer Hardware ◽

Computer Algorithms ◽

Low Contrast ◽

Specific Proteins ◽

Effective Use ◽

Single Living Cells

Advances in digital imaging microscopy and in the synthesis of fluorescent dyes allow the determination of 3D distribution of specific proteins, ions, GNA or DNA in single living cells. Effective use of this technology requires a combination of optical and computer hardware and software for image restoration, feature extraction and computer graphics.The digital imaging microscope consists of a conventional epifluorescence microscope with computer controlled focus, excitation and emission wavelength and duration of excitation. Images are recorded with a cooled (-80°C) CCD. 3D images are obtained as a series of optical sections at .25 - .5 μm intervals.A conventional microscope has substantial blurring along its optical axis. Out of focus contributions to a single optical section cause low contrast and flare; details are poorly resolved along the optical axis. We have developed new computer algorithms for reversing these distortions. These image restoration techniques and scanning confocal microscopes yield significantly better images; the results from the two are comparable.

Download Full-text

Improving x-ray map resolution with image restoration techniques

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100165458 ◽

1996 ◽

Vol 54 ◽

pp. 598-599

Author(s):

Richard B. Mott ◽

John J. Friel ◽

Charles G. Waldman

Keyword(s):

Image Restoration ◽

Hubble Space Telescope ◽

Extensive Literature ◽

Small Object ◽

X Rays ◽

X Ray ◽

Mapping Parameters ◽

The Matrix ◽

The 1960S ◽

Map Resolution

X-rays are emitted from a relatively large volume in bulk samples, limiting the smallest features which are visible in X-ray maps. Beam spreading also hampers attempts to make geometric measurements of features based on their boundaries in X-ray maps. This has prompted recent interest in using low voltages, and consequently mapping L or M lines, in order to minimize the blurring of the maps.An alternative strategy draws on the extensive work in image restoration (deblurring) developed in space science and astronomy since the 1960s. A recent example is the restoration of images from the Hubble Space Telescope prior to its new optics. Extensive literature exists on the theory of image restoration. The simplest case and its correspondence with X-ray mapping parameters is shown in Figures 1 and 2.Using pixels much smaller than the X-ray volume, a small object of differing composition from the matrix generates a broad, low response. This shape corresponds to the point spread function (PSF). The observed X-ray map can be modeled as an “ideal” map, with an X-ray volume of zero, convolved with the PSF. Figure 2a shows the 1-dimensional case of a line profile across a thin layer. Figure 2b shows an idealized noise-free profile which is then convolved with the PSF to give the blurred profile of Figure 2c.

Download Full-text