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

He, Lirong; Cui, Guangmang; Feng, Huanqing; Xu, Zhihai; Li, Qi; Chen, Yueting

doi:10.1016/j.optcom.2014.11.021

Cited by 12 publications

(4 citation statements)

References 19 publications

(18 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the above equation: μx stands for the mean value of x; μ y stands for the mean value of y; σ stands for the variance of x; σ xy stands for the covariance of x and y; c 1 = (k 1 L) 2 and c 2 = (k 2 L) 2 are two constants, which are used to maintain stability; L stands for the dynamic range of the pixel value; k 1 = 0.01, k 2 = 0.03.…”

Section: Quality Evaluationmentioning

confidence: 99%

“…With the development of artificial intelligence (AI), digital image restoration has become a crucial task in computer vision [1][2]. Digital image restoration is an image processing technology where the undamaged information in the image is used to restore the missing content in the image or remove the specific information in the image under the premise that the quality of the image and its natural effects are not damaged [3][4].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Image and Graph Restoration Dependent on Generative Adversarial Network Algorithm

Cao

2021

Teh. vjesn.

View full text Add to dashboard Cite

Section: Quality Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Image and Graph Restoration Dependent on Generative Adversarial Network Algorithm

Cao

2021

Teh. vjesn.

View full text Add to dashboard Cite

“…In section 1.2 we fix the terminology needed to justify the plan of the paper, which is given in section 1.3. opens/closes according to a pseudorandom binary code called flutter shutter code. Following the flutter shutter literature [3,4,6,11,17,18,19,28,29,30,31,37,38,39,43,47,48,50,56], only well-posed deconvolution strategies will be compared. Indeed, if the flutter shutter code is well chosen, arbitrarily severe uniform motion blurs are made invertible.…”

Section: Middle: the Image Reconstructed By Direct Deconvolution Rigmentioning

confidence: 99%

“…As usual in the literature on the flutter shutter [3,4,6,11,17,18,19,28,29,30,31,37,38,39,48,56], only well-posed deconvolution strategies are considered. Thus, we always assume that the flutter shutter gain function α does not vanish on Ö¡π v , π v × and that the velocity v È Ê is a known constant.…”

Section: 2mentioning

confidence: 99%

A Theory of Optimal Flutter Shutter for Probabilistic Velocity Models

Tendero¹,

Morel²

2016

SIAM J. Imaging Sci.

View full text Add to dashboard Cite

Abstract. Flutter shutter (coded exposure) is a new paradigm for cameras that allows for an arbitrary increase of the exposure time when the relative camera/scene motion is uniform. The photon flux is interrupted according to a flutter shutter code. For arbitrarily severe uniform motion blur a well chosen code guarantees an invertible blur kernel. Yet, when the relative camera/scene velocity is a known constant, a flutter shutter cannot gain more than a 1.17 factor in terms of root mean-squared error compared to the optimal snapshot. In this paper, we prove that this optimality bound can be relaxed under the realistic assumption that a random model for the velocities is available. We give analytical formulae for the optimal flutter shutter code and the optimal snapshots associated with a random velocity distribution. Conversely we also prove formulae that reveal the velocity distribution underlying a given flutter shutter code. 1. Introduction. Digital cameras count at each pixel sensor the number of photons emitted by the observed scene during a time span called the exposure time. The photon count follows a Poisson random variable. Its mean is the ideal (noiseless) pixel value. The difference between this ideal pixel value and the observed sensor photon count is called (shot) noise. The ratio of the mean of the photon count over its standard-deviation is called the signal-to-noise ratio (SNR). In passive imaging systems there is no control over the scene lighting. Thus, the only safe way to increase the SNR is to integrate more photons by increasing the exposure time. Yet, when the scene or the camera moves during the exposure process, the resulting images are degraded by a motion blur. If the support of a motion blur kernel exceeds two pixels, the blur is no longer invertible. This limits the exposure time and therefore the image quality of a snapshot. A setup solving this photography trade-off between noise and blur was proposed in [3,4,6,37,38,39] for uniform camera-scene motions. The authors of these papers attached a flutter shutter to a camera to get an invertible motion blur kernel. A flutter shutter

show abstract