Nonuniformity Correction Based on Adaptive Sparse Representation Using Joint Local and Global Constraints Based Learning Rate

Liu, Zhe; Ma, Yong; Fan, Fan; Ma, Jiayi

doi:10.1109/access.2018.2799606

Cited by 6 publications

(5 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where ( x , y ) stands for the image plane coordinates, θ represents the viewing angle; v ( x , y ) represents the vignetting coefficient, τ ( x , y ) represents the transmittance coefficient relative to the centre view and E straylight ( x , y ) represents stray radiation on the image plane. Although stray light on a n × m pixel camera would generally be descried by a ( n × m ) 2 size matrix, the model focused on the total stray radiation on image plane. It produces a fixed pattern when the imaging condition is stationary.…”

Section: Theorymentioning

confidence: 99%

“…The nonuniformity correction (NUC) of an imaging system response has been an important issue in the infrared image processing domain since the 1980s. [1][2][3][4][5] However, the nonuniformity of colour imaging systems also leads to obvious measurement errors in photometric and colorimetric measurements. 6,7 The NUC methods of an imaging system response can be divided into two main categories: scene-based [8][9][10][11][12][13][14][15][16][17][18] and calibration-based [19][20][21][22][23][24][25][26][27][28][29][30] methods.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Response nonuniformity modelling for colour imaging systems

Xie

Sun

Gong

et al. 2023

Lighting Research & Technology

View full text Add to dashboard Cite

In this paper, a theoretical analysis reveals that system radiation transmission nonuniformity and system radiation conversion nonuniformity have different forms. A colour imaging system response nonuniformity model is then proposed according to the two kind of nonuniformity of three channels. The model includes two models, one for brightness and one for chromaticity. The brightness model is built according to the nonuniformity of single channel. The chromaticity model is built as an error ellipse in the chromaticity diagram. The error ellipse can be also used as a representation parameter of the radiation conversion nonuniformity. The proposed models were verified using three commercial digital cameras. The experimental results show that the proposed nonuniformity model is correct. Moreover, the proposed representation parameter is an effective way to evaluate and characterize the nonuniformity of a colour imaging system. This research has value as a reference for the correction and evaluation of response nonuniformity in colour imaging systems.

show abstract

Section: Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Response nonuniformity modelling for colour imaging systems

Xie

Sun

Gong

et al. 2023

Lighting Research & Technology

View full text Add to dashboard Cite

show abstract

“…As typical member of this category is BLS-GSM (Bayes Least Square Gaussian Scale Mixture). This approach translates the image denoising problem into an inverse problem with the use of Bayesian minimisation [ 42 , 43 ].…”

Section: Recent Workmentioning

confidence: 99%

Towards to Optimal Wavelet Denoising Scheme—A Novel Spatial and Volumetric Mapping of Wavelet-Based Biomedical Data Smoothing

Stanke

Kubíček

Vilímek

et al. 2020

Sensors

View full text Add to dashboard Cite

Wavelet transformation is one of the most frequent procedures for data denoising, smoothing, decomposition, features extraction, and further related tasks. In order to perform such tasks, we need to select appropriate wavelet settings, including particular wavelet, decomposition level and other parameters, which form the wavelet transformation outputs. Selection of such parameters is a challenging area due to absence of versatile recommendation tools for suitable wavelet settings. In this paper, we propose a versatile recommendation system for prediction of suitable wavelet selection for data smoothing. The proposed system is aimed to generate spatial response matrix for selected wavelets and the decomposition levels. Such response enables the mapping of selected evaluation parameters, determining the efficacy of wavelet settings. The proposed system also enables tracking the dynamical noise influence in the context of Wavelet efficacy by using volumetric response. We provide testing on computed tomography (CT) and magnetic resonance (MR) image data and EMG signals mostly of musculoskeletal system to objectivise system usability for clinical data processing. The experimental testing is done by using evaluation parameters such is MSE (Mean Squared Error), ED (Euclidean distance) and Corr (Correlation index). We also provide the statistical analysis of the results based on Mann-Whitney test, which points out on statistically significant differences for individual Wavelets for the data corrupted with Salt and Pepper and Gaussian noise.

show abstract

“…Also, the scene based NUC techniques estimate the global translation between several adjacent frames and computes the errors by various methods such as multi frame registration and neural network techniques. Neural network based methods adopt spatial filters and sparse representation theory 9 . These algorithms are difficult to implement in real time and they do not provide the required radiometric accuracy.…”

Section: Scene Based Non Uniformity Correction Techniquesmentioning

confidence: 99%

Real Time Non uniformity Correction Algorithm and Implementation in Reconfigurable Architecture for Infra red Imaging Systems

2019

View full text Add to dashboard Cite

In modern electro-optical systems, infra-red (IR) imaging system is an essential sensor used for day and night surveillance. In recent years, advancements in IR sensor technology resulted the detectors having smaller pitch, better thermal sensitivity with large format like 640.512, 1024.768 and 1280.1024. Large format IR detectors enables realisation of high resolution compact thermal imager having wide field-of view coverage. However, the performance of these infrared imaging systems gets limited by non uniformity produced by sensing element, which is temporal in nature and present in spatial domain. This non uniformity results the fixed pattern noise, which arises due to variation in gain and offset components of the each pixel of the sensor even when exposed to a uniform scene. This fixed pattern noise limits the temperature resolution capability of the IR imaging system thereby causing the degradation in system performance. Therefore, it is necessary to correct the non-uniformities in real time. In this paper, non uniformity correction algorithm and its implementation in reconfigurable architectures have been presented and results on real time data have been described.

show abstract

Nonuniformity Correction Based on Adaptive Sparse Representation Using Joint Local and Global Constraints Based Learning Rate

Cited by 6 publications

References 26 publications

Response nonuniformity modelling for colour imaging systems

Response nonuniformity modelling for colour imaging systems

Towards to Optimal Wavelet Denoising Scheme—A Novel Spatial and Volumetric Mapping of Wavelet-Based Biomedical Data Smoothing

Real Time Non uniformity Correction Algorithm and Implementation in Reconfigurable Architecture for Infra red Imaging Systems

Contact Info

Product

Resources

About