scholarly journals Joint Demosaicing and Denoising Based on Interchannel Nonlocal Mean Weighted Moving Least Squares Method

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4697 ◽  
Author(s):  
Yeahwon Kim ◽  
Hohyung Ryu ◽  
Sunmi Lee ◽  
Yeon Ju Lee
Keyword(s):  
Polynomial Approximation ◽  
Least Squares Method ◽  
Color Image ◽  
Noise Removal ◽  
Image Sensor ◽  
Least Square ◽  
Color Filter ◽  
Moving Least Square ◽  
Pixel Sensors ◽  
Color Channels

Nowadays, the sizes of pixel sensors in digital cameras are decreasing as the resolution of the image sensor increases. Due to the decreased size, the pixel sensors receive less light energy, which makes it more sensitive to thermal noise. Even a small amount of noise in the color filter array (CFA) can have a significant effect on the reconstruction of the color image, as two-thirds of the missing data would have to be reconstructed from noisy data; because of this, direct denoising would need to be performed on the raw CFA to obtain a high-resolution color image. In this paper, we propose an interchannel nonlocal weighted moving least square method for the noise removal of the raw CFA. The proposed method is our first attempt of applying a two dimensional (2-D) polynomial approximation to denoising the CFA. Previous works make use of 2-D linear or directional 1-D polynomial approximations. The reason that 2-D polynomial approximation methods have not been applied to this problem is the difficulty of the weight control in the 2-D polynomial approximation method, as a small amount of noise can have a large effect on the approximated 2-D shape. This makes CFA denoising more important, as the approximated 2-D shape has to be reconstructed from only one-third of the original data. To address this problem, we propose a method that reconstructs the approximated 2-D shapes corresponding to the RGB color channels based on the measure of the similarities of the patches directly on the CFA. By doing so, the interchannel information is incorporated into the denoising scheme, which results in a well-controlled and higher order of polynomial approximation of the color channels. Compared to other nonlocal-mean-based denoising methods, the proposed method uses an extra reproducing constraint, which guarantees a certain degree of the approximation order; therefore, the proposed method can reduce the number of false reconstruction artifacts that often occur in nonlocal-mean-based denoising methods. Experimental results demonstrate the performance of the proposed algorithm.

scholarly journals Noise Removal in the Developing Process of Digital Negatives

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 902
Author(s):  
Marek Szczepański ◽  
Filip Giemza
Keyword(s):  
Noise Reduction ◽  
Color Image ◽  
Image Acquisition ◽  
Noise Removal ◽  
Image Sensor ◽  
Sensor Data ◽  
Color Filter ◽  
Local Means ◽  
Real Process ◽  
Cmos Sensor

Most modern color digital cameras are equipped with a single image sensor with a color filter array (CFA). One of the most important stages of preprocessing is noise reduction. Most research related to this topic ignores the problem associated with the actual color image acquisition process and assumes that we are processing the image in the sRGB space. In the presented paper, the real process of developing raw images obtained from the CFA sensor was analyzed. As part of the work, a diverse database of test images in the form of a digital negative and its reference version was prepared. The main problem posed in the work was the location of the denoising and demosaicing algorithms in the entire raw image processing pipeline. For this purpose, all stages of processing the digital negative are reproduced. The process of noise generation in the image sensors was also simulated, parameterizing it with ISO sensitivity for a specific CMOS sensor. In this work, we tested commonly used algorithms based on the idea of non-local means, such as NLM or BM3D, in combination with various techniques of interpolation of CFA sensor data. Our experiments have shown that the use of noise reduction methods directly on the raw sensor data, improves the final result only in the case of highly disturbed images, which corresponds to the process of image acquisition in difficult lighting conditions.

Color-filter arrays for silicon solid-state image arrays sensors

1991 ◽  
Vol 69 (3-4) ◽  
pp. 543-548 ◽  
Author(s):  
Sheldon J. Hood ◽  
Savvas G. Chamberlain
Keyword(s):  
Polymer Films ◽  
Color Image ◽  
Spectral Response ◽  
Incident Light ◽  
Spectral Characteristics ◽  
Image Sensor ◽  
Silicon Wafers ◽  
Organic Polymer ◽  
Color Filter ◽  
Film Material

A monolithic fabrication process for color-filter arrays was developed. The color-filter arrays were composed of a blue, green, and red colored mosaic of transparent film elements. The color-filter arrays were fabricated on silicon wafers on which linear arrays of silicon photodiodes had previously been fabricated. Different colored film elements overlaid different photodiodes so that the spectral response of any photodiode was the produce of its intrinsic response and the transmittance characteristic of the color filter. This technology is applicable to the development of color image sensor arrays. The color-filter arrays utilized a transparent, organic polymer film base as a support for dyes. Organic solvent dyes were chosen to impart color into the film material. Solvent spin-casting techniques were used to coat silicon wafers with polymer films of different colors. The polymer films were patterned by selectively etching the films in an oxygen plasma through an aluminum mask. Measurements were performed on the color-filter-covered photodiodes to determine their spectral response as a function of the wavelength of the incident light. The measurements showed that the color-filter arrays had good color spectral characteristics.

scholarly journals Joint Demosaicing and Denoising Based on a Variational Deep Image Prior Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2970 ◽  
Author(s):  
Yunjin Park ◽  
Sukho Lee ◽  
Byeongseon Jeong ◽  
Jungho Yoon
Keyword(s):  
Color Image ◽  
Image Sensor ◽  
Training Data ◽  
Color Filter ◽  
Color Filter Array ◽  
Image Prior ◽  
Filter Array ◽  
Monochrome Image ◽  
Deep Image ◽  
Different Levels

A joint demosaicing and denoising task refers to the task of simultaneously reconstructing and denoising a color image from a patterned image obtained by a monochrome image sensor with a color filter array. Recently, inspired by the success of deep learning in many image processing tasks, there has been research to apply convolutional neural networks (CNNs) to the task of joint demosaicing and denoising. However, such CNNs need many training data to be trained, and work well only for patterned images which have the same amount of noise they have been trained on. In this paper, we propose a variational deep image prior network for joint demosaicing and denoising which can be trained on a single patterned image and works for patterned images with different levels of noise. We also propose a new RGB color filter array (CFA) which works better with the proposed network than the conventional Bayer CFA. Mathematical justifications of why the variational deep image prior network suits the task of joint demosaicing and denoising are also given, and experimental results verify the performance of the proposed method.

CMOS Image Sensor Performance from Different Order of Interpolation and Noise Reduction

2012 ◽  
Vol 588-589 ◽  
pp. 668-671
Author(s):  
Xuan Li ◽  
Xiao Han ◽  
Le Le Qu ◽  
Rui Guo
Keyword(s):  
Noise Reduction ◽  
Least Squares Method ◽  
Digital Camera ◽  
Complementary Metal Oxide Semiconductor ◽  
Objective Evaluation ◽  
Cmos Image Sensor ◽  
Image Sensor ◽  
Oxide Semiconductor ◽  
Color Filter ◽  
Different Order

Three noise reduction programs based on Color Filter Array (CFA) image from Complementary Metal-Oxide-Semiconductor (CMOS) image sensor system are proposed. Imaging performance from different order of interpolation and noise reduction is researched. The relevant models are established and three interpolation methods and four noise reduction methods are selected in the paper. The results show that under different imaging sequence, whether from objective evaluation or from the subjective point of view, Linear Filtering noise reduction is the worst, noise reduction on Least Squares Method is optimal. The conclusions have references to digital camera imaging.

A practical approach on non-regular sampling and universal demosaicing of raw image sensor data

2020 ◽  
Vol 2020 (1) ◽  
pp. 91-95
Author(s):  
Philipp Backes ◽  
Jan Fröhlich
Keyword(s):  
Image Quality ◽  
Source Code ◽  
Image Sensor ◽  
Sensor Data ◽  
Color Filter ◽  
Lowpass Filter ◽  
Sampling Errors ◽  
Regular Sampling ◽  
Single Sensor ◽  
Similar Image Quality

Non-regular sampling is a well-known method to avoid aliasing in digital images. However, the vast majority of single sensor cameras use regular organized color filter arrays (CFAs), that require an optical-lowpass filter (OLPF) and sophisticated demosaicing algorithms to suppress sampling errors. In this paper a variety of non-regular sampling patterns are evaluated, and a new universal demosaicing algorithm based on the frequency selective reconstruction is presented. By simulating such sensors it is shown that images acquired with non-regular CFAs and no OLPF can lead to a similar image quality compared to their filtered and regular sampled counterparts. The MATLAB source code and results are available at: http://github. com/PhilippBackes/dFSR

Fast noise removal algorithm for HDR image based on least square method

2009 ◽  
Vol 29 (4) ◽  
pp. 994-996
Author(s):  
De-quan SUN ◽  
Jun ZHANG ◽  
Xiao-feng LI ◽  
Hui LI
Keyword(s):  
Least Square Method ◽  
Noise Removal ◽  
Least Square ◽  
Hdr Image

Research on Generator Excitation Parameter Identification - PSS Low-Pass Link Parameter Identification

2013 ◽  
Vol 805-806 ◽  
pp. 716-720
Author(s):  
Tao Xu ◽  
Tian Long Shao ◽  
Dong Fang Zhang
Keyword(s):  
Mathematical Model ◽  
Parameter Identification ◽  
Least Squares Method ◽  
Least Square Method ◽  
Least Square ◽  
National Standards ◽  
Matlab Software ◽  
Low Pass ◽  
Newton Iterations ◽  
The Mathematical Model

Combined with the contents of the study-PSS low-pass link parameter identification. Least-squares method is selected. Using least-square method for PSS low-pass link mathematical model are also deduced. For the results, because of the mathematical model is solving nonlinear equations, cannot used by the Newton method directly. So we choose to use Newton iterations, with this feature, choose to use MATLAB software to solve the equation. Identification of the use of MATLAB software lags after the PSS parameters obtained recognition results compared with national standards, identifying and verifying the practicability.

Cellular Neural Network Based on Hybrid Linear Matrix Inequality and Particle Swarm Optimization for Noise Removal of Color Image

2011 ◽  
Vol 422 ◽  
pp. 771-774
Author(s):  
Te Jen Su ◽  
Jui Chuan Cheng ◽  
Yu Jen Lin
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Linear Matrix Inequality ◽  
Color Image ◽  
Particle Swarm ◽  
Cellular Neural Network ◽  
Noise Removal ◽  
Linear Matrix ◽  
Matrix Inequality ◽  
Swarm Optimization

This paper presents a color image noise removal technique that employs a cellular neural network (CNN) based on hybrid linear matrix inequality (LMI) and particle swarm optimization (PSO). For designing templates of CNN, the Lyapunov stability theorem is applied to derive the criterion for the uniqueness and global asymptotic stability of the CNN’s equilibrium point. The template design is characterized as a standard LMI problem, and the parameters of templates are optimized by PSO. The input templates are obtained by employing the CNN’s property of saturation nonlinearity, which can be used to eliminate noise from arbitrary corrupted images. The demonstrated examples are compared favorably with other available methods, which illustrate the better performance of the proposed LMI-PSO-CNN methodology.

Research on the Wire Drawing Die Pass Parameter Calculation Based on the Least Square Algorithm

2012 ◽  
Vol 591-593 ◽  
pp. 850-853
Author(s):  
Huai Xing Wen ◽  
Yong Tao Yang
Keyword(s):  
Pore Structure ◽  
Least Squares ◽  
Least Squares Method ◽  
Piecewise Linear ◽  
Wire Drawing ◽  
Least Square ◽  
Structure Characteristics ◽  
Drawing Die ◽  
The Wire ◽  
Drawing Dies

Drawing Dies meter A / D acquisition module will be collected from the mold hole contour data to draw a curve in Matlab. According to the mold pore structure characteristics of the curve, the initial cut-off point of each part of contour is determined and iteratived optimization to find the best cut-off point, use the least squares method for fitting piecewise linear and fitting optimization to find the function of the various parts of the curve function, finally calculate the pass parameters of drawing mode. Parameters obtained compare with the standard mold, both of errors are relatively small that prove the correctness of the algorithm. Also a complete algorithm flow of pass parameters is designed, it can fast and accurately measure the wire drawing die hole parameters.

Fuzzy C-Regression Models

2013 ◽  
Vol 278-280 ◽  
pp. 1323-1326
Author(s):  
Yan Hua Yu ◽  
Li Xia Song ◽  
Kun Lun Zhang
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Linear Models ◽  
Least Squares Method ◽  
Least Square Method ◽  
Least Square ◽  
Estimation Methods ◽  
Statistical Regression ◽  
Fuzzy Linear Regression ◽  
Fuzzy Degree

Fuzzy linear regression has been extensively studied since its inception symbolized by the work of Tanaka et al. in 1982. As one of the main estimation methods, fuzzy least squares approach is appealing because it corresponds, to some extent, to the well known statistical regression analysis. In this article, a restricted least squares method is proposed to fit fuzzy linear models with crisp inputs and symmetric fuzzy output. The paper puts forward a kind of fuzzy linear regression model based on structured element, This model has precise input data and fuzzy output data, Gives the regression coefficient and the fuzzy degree function determination method by using the least square method, studies the imitation degree question between the observed value and the forecast value.

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