Noise Model of a Multispectral TDI CCD Imaging System and Its Parameter Estimation of Piecewise Weighted Least Square Fitting

Zheng, Li; Jin, Guang; Xu, Wei; Qu, Hongsong; Wu, Yong

doi:10.1109/jsen.2017.2696562

Cited by 14 publications

(4 citation statements)

References 27 publications

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“…Unsharp mask filter (UMF), a high-pass linear filtering method, is a typical edge enhancing algorithm with very low-cost computational structure [24][25][26]. The neighborhood of the filtering operator is a fixed 3 × 3 matrix, as shown in Equation (6), where υ determines the direction of the edge sharpening and belongs to the interval [0, 1]. If υ = 0, the filter will sharpen the image in the horizontal and vertical directions, respectively.…”

Section: Umf Filtermentioning

confidence: 99%

“…There are many kinds of noises interfering the imaging system, principally from the photoelectric detectors, for example, photon shot noise, dark current noise, thermal noise, and so on. The images acquired by the system always include noise, which affects their final display effect [ 5 , 6 , 7 ]. Therefore, some image preprocessing techniques can be applied to obtain the image data of high definition and high signal-to-noise ratio (SNR) by denoising and edge sharpening.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Adaptive Spatial Preprocessing Method for Remote Sensing Images

Zheng

2021

Sensors

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Since remote sensing images are one of the main sources for people to obtain required information, the quality of the image becomes particularly important. Nevertheless, noise often inevitably exists in the image, and the targets are usually blurred by the acquisition of the imaging system, resulting in the degradation of quality of the images. In this paper, a novel preprocessing algorithm is proposed to simultaneously smooth noise and to enhance the edges, which can improve the visual quality of remote sensing images. It consists of an improved adaptive spatial filter, which is a weighted filter integrating functions of both noise removal and edge sharpness. Its processing parameters are flexible and adjustable relative to different images. The experimental results confirm that the proposed method outperforms the existing spatial algorithms both visually and quantitatively. It can play an important role in the remote sensing field in order to achieve more information of interested targets.

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Section: Umf Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Adaptive Spatial Preprocessing Method for Remote Sensing Images

Zheng

2021

Sensors

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“…In recent years, with the gradual development of Earth remote sensing, astronomical observation, lunar remote sensing and deep space exploration missions, higher demands have been put forward for space optical imaging payloads. As a detector which is used widely in remote sensing field because of its high sensitivity and high signal-to-noise ratio (SNR), TDI CCD and its signal processing path are the core of photoelectric conversion in the imaging system, which directly affects the key performance of the system [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Compensation for the non‐uniformity correction of multi‐channel TDI CCD under variable gain

Wang

2021

IET Optoelectronics

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The compensation for non‐uniformity correction of multi‐channel time delay integration charge coupled device (TDI CCD) under variable gain is presented. The mathematical model of photoelectric conversion is established first, and, then, a two‐step non‐uniformity correction method for multi‐channel TDI CCD is proposed. Based on this method, a compensation algorithm for non‐uniformity correction under variable gain is elaborated in detail. The experimental results show that the proposed algorithm can always maintain the photo response non‐uniformity (PRNU) of the corrected images better than 1.14% when the programmable gain amplification (PGA) gain varies within the range of 1–3, compared to the conventional method of degrading to 2.91%. Meanwhile, a compression method for correction coefficients under multiple integral stages is proposed, which can reduce the storage space requirement by 50% at the cost of the PRNU being degraded by a maximum of 0.19% at all integral stages.

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“…The scatterplot fitting-based methods are proposed to group a set of local means and variances of the selected blocks, and to fit those scatter points using linear regression [28,29,30]. The least square fitting-based methods [31,32,33] use a least square approach to estimate PGN parameters by sampling weak textured blocks and fitting a group of sampled data. In [34,35], the maximum likelihood estimation-based methods still extract smooth blocks first, and then exploit the maximum likelihood function to estimate PGN parameters.…”

Section: Introductionmentioning

confidence: 99%

Noise Estimation for Image Sensor Based on Local Entropy and Median Absolute Deviation

Wei

et al. 2019

Sensors

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Noise estimation for image sensor is a key technique in many image pre-processing applications such as blind de-noising. The existing noise estimation methods for additive white Gaussian noise (AWGN) and Poisson-Gaussian noise (PGN) may underestimate or overestimate the noise level in the situation of a heavy textured scene image. To cope with this problem, a novel homogenous block-based noise estimation method is proposed to calculate these noises in this paper. Initially, the noisy image is transformed into the map of local gray statistic entropy (LGSE), and the weakly textured image blocks can be selected with several biggest LGSE values in a descending order. Then, the Haar wavelet-based local median absolute deviation (HLMAD) is presented to compute the local variance of these selected homogenous blocks. After that, the noise parameters can be estimated accurately by applying the maximum likelihood estimation (MLE) to analyze the local mean and variance of selected blocks. Extensive experiments on synthesized noised images are induced and the experimental results show that the proposed method could not only more accurately estimate the noise of various scene images with different noise levels than the compared state-of-the-art methods, but also promote the performance of the blind de-noising algorithm.

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Noise Model of a Multispectral TDI CCD Imaging System and Its Parameter Estimation of Piecewise Weighted Least Square Fitting

Cited by 14 publications

References 27 publications

An Improved Adaptive Spatial Preprocessing Method for Remote Sensing Images

An Improved Adaptive Spatial Preprocessing Method for Remote Sensing Images

Compensation for the non‐uniformity correction of multi‐channel TDI CCD under variable gain

Noise Estimation for Image Sensor Based on Local Entropy and Median Absolute Deviation

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