Fast approach to infrared image restoration based on shrinkage functions calibration

Zhang, Chengshuo; Shi, Zelin; Xu, Bin; Feng, Bin

doi:10.1117/1.oe.55.5.053107

Cited by 2 publications

(1 citation statement)

References 31 publications

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“…This experiment with athermalization range of 100°C is only target observation in laboratory and its decoded images at high-low temperature are serious in artefacts. Our previous works report thermal effect [8], design and manufacture of optical phase masks [2,9], a proposed decoding method based on shrinkage function [10], and a developed wavevfront coding athermalized infrared imaging [9]. This paper will further make a quantitative validation of our wavefront coding athermalized infrared imaging system in three aspects of athermalization temperature range, extension of focal depth, approximation to with in-focus image.…”

Section: Introductionmentioning

confidence: 99%

110 °C range athermalization of wavefront coding infrared imaging systems

Feng

Shi

Chang

et al. 2017

Infrared Physics & Technology

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Section: Introductionmentioning

confidence: 99%

110 °C range athermalization of wavefront coding infrared imaging systems

Feng

Shi

Chang

et al. 2017

Infrared Physics & Technology

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Total variation with overlapping group sparsity and Lp quasinorm for infrared image deblurring under salt-and-pepper noise

Chen

Peng

Wu³

2019

J. Electron. Imag.

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Because of the limitations of the infrared imaging principle and the properties of infrared imaging systems, infrared images have some drawbacks, including a lack of details, indistinct edges, and a large amount of salt-andpepper noise. To improve the sparse characteristics of the image while maintaining the image edges and weakening staircase artifacts, this paper proposes a method that uses the Lp quasinorm instead of the L1 norm and for infrared image deblurring with an overlapping group sparse total variation method. The Lp quasinorm introduces another degree of freedom, better describes image sparsity characteristics, and improves image restoration. Furthermore, we adopt the accelerated alternating direction method of multipliers and fast Fourier transform theory in the proposed method to improve the efficiency and robustness of our algorithm. Experiments show that under different conditions for blur and salt-and-pepper noise, the proposed method leads to excellent performance in terms of objective evaluation and subjective visual results. detector itself is unavoidable. According to the mechanism that produces it, noise can be divided into thermal noise, shot noise, photon noise, and other types. The part of the noise that has a large influence on the image can be considered to be equivalent to Gaussian white noise and salt-andpepper noise. In addition, during image capture, degradation of the observed image can be caused by various factors such as defocusing, diffraction, relative motion between the detector and the object.Image restoration is the improvement of the quality of a degraded image. It removes or mitigates the degradation of the image quality that occurs during the acquisition of the digital image in order to visually improve the image. The most typical degradation phenomena are blur and noise. This paper mainly discusses the restoration of blurry images, that is, deblurring.The blurring process of the image can be expressed as a convolution of the original image with the blur kernel and superimposed noise, that is g = h * f + n, where * is the convolution operator, g denotes a blurred image containing noise, f denotes the original image, h is a blur kernel, also called a point spread function (PSF), and n is noise. The inverse processing of a blurred image is called image deconvolution, and its purpose is to recover a clear image from the blurred image.According to whether the PSF is known, the image deconvolution problem is divided into two types: blind deconvolution and non-blind deconvolution.Non-blind image deconvolution assumes that both the blurred image and blur kernel for estimating a clear image have been given. In image restoration processing, non-blind image deconvolution is an ill-conditioned inverse problem that is often modeled by the regularization method as

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Fast approach to infrared image restoration based on shrinkage functions calibration

Cited by 2 publications

References 31 publications

110 °C range athermalization of wavefront coding infrared imaging systems

110 °C range athermalization of wavefront coding infrared imaging systems

Total variation with overlapping group sparsity and Lp quasinorm for infrared image deblurring under salt-and-pepper noise

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