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 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.

Spectral Characteristics and Surface Morphology of Organic Polymer Films Containing Vanadium Pentoxide Nanoparticles

2004 ◽  
Vol 40 (3) ◽  
pp. 259-266 ◽  
Author(s):  
S. V. Svechnikov ◽  
V. D. Pokhodenko ◽  
N. F. Guba ◽  
L. I. Fenenko ◽  
P. S. Smertenko ◽  
...  
Keyword(s):  
Surface Morphology ◽  
Vanadium Pentoxide ◽  
Polymer Films ◽  
Spectral Characteristics ◽  
Organic Polymer

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.

scholarly journals Data-Driven Convolutional Model for Digital Color Image Demosaicing

2021 ◽  
Vol 11 (21) ◽  
pp. 9975
Author(s):  
Francesco de Gioia ◽  
Luca Fanucci
Keyword(s):  
Color Image ◽  
Random Noise ◽  
Spectral Characteristics ◽  
Model Performance ◽  
Data Driven ◽  
Quantization Noise ◽  
Color Filter ◽  
Parallel Composition ◽  
Color Filter Array ◽  
Filter Array

Modern digital cameras use specific arrangement of Color Filter Array to sample light wavelength corresponding to visible colors. The most common Color Filter Array is the Bayer filter that samples only one color per pixel. To recover the full resolution image, an interpolation algorithm can be used. This process is called demosaicing and it is one of the first processing stages of a digital imaging pipeline. We introduce a novel data-driven model for demosaicing that takes into account the different requirements for reconstruction of the image Luma and Chrominance channels. The final model is a parallel composition of two reconstruction networks with individual architecture and trained with distinct loss functions. In order to solve the overfitting problem, we prepared a dataset that contains groups of patches that share common chromatic and spectral characteristics. We reported the reconstruction error on noise-free images and measured the effect of random noise and quantization noise in the demosaicing reconstruction. To test our model performance, we implemented the network on NVIDIA Jetson Nano, obtaining an end-to-end running time of less than one second for a full frame 12 MPixel image.

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.

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

scholarly journals Hyperspectral Data Simulation (Sentinel-2 to AVIRIS-NG) for Improved Wildfire Fuel Mapping, Boreal Alaska

2021 ◽  
Vol 13 (9) ◽  
pp. 1693
Author(s):  
Anushree Badola ◽  
Santosh K. Panda ◽  
Dar A. Roberts ◽  
Christine F. Waigl ◽  
Uma S. Bhatt ◽  
...  
Keyword(s):  
Land Cover ◽  
Spectral Resolution ◽  
Hyperspectral Image ◽  
Spectral Response ◽  
Spectral Characteristics ◽  
Hyperspectral Data ◽  
Spectral Unmixing ◽  
High Spectral Resolution ◽  
Fuel Mapping ◽  
Sentinel 2

Alaska has witnessed a significant increase in wildfire events in recent decades that have been linked to drier and warmer summers. Forest fuel maps play a vital role in wildfire management and risk assessment. Freely available multispectral datasets are widely used for land use and land cover mapping, but they have limited utility for fuel mapping due to their coarse spectral resolution. Hyperspectral datasets have a high spectral resolution, ideal for detailed fuel mapping, but they are limited and expensive to acquire. This study simulates hyperspectral data from Sentinel-2 multispectral data using the spectral response function of the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor, and normalized ground spectra of gravel, birch, and spruce. We used the Uniform Pattern Decomposition Method (UPDM) for spectral unmixing, which is a sensor-independent method, where each pixel is expressed as the linear sum of standard reference spectra. The simulated hyperspectral data have spectral characteristics of AVIRIS-NG and the reflectance properties of Sentinel-2 data. We validated the simulated spectra by visually and statistically comparing it with real AVIRIS-NG data. We observed a high correlation between the spectra of tree classes collected from AVIRIS-NG and simulated hyperspectral data. Upon performing species level classification, we achieved a classification accuracy of 89% for the simulated hyperspectral data, which is better than the accuracy of Sentinel-2 data (77.8%). We generated a fuel map from the simulated hyperspectral image using the Random Forest classifier. Our study demonstrated that low-cost and high-quality hyperspectral data can be generated from Sentinel-2 data using UPDM for improved land cover and vegetation mapping in the boreal forest.

scholarly journals Formation of half-period surface relief gratings in azobenzene containing polymer films

2020 ◽  
Vol 126 (9) ◽  
Author(s):  
Joachim Jelken ◽  
Carsten Henkel ◽  
Svetlana Santer
Keyword(s):  
Polymer Film ◽  
Polymer Films ◽  
Surface Relief ◽  
Incident Light ◽  
Glassy State ◽  
Polarized Light ◽  
Half Period ◽  
Polarization Distribution ◽  
Linearly Polarized ◽  
Set Up

Abstract We study the peculiar response of photo-sensitive polymer films irradiated with a certain type of interference pattern where one interfering beam is S-polarized, while the second one is P-polarized. The polymer film, although in a glassy state, deforms following the local polarization distribution of the incident light, and a surface relief grating (SRG) appears whose period is half the optical one. All other types of interference patterns result in the matching of both periods. The topographical response is triggered by the alignment of photo-responsive azobenzene containing polymer side chains orthogonal to the local electrical field, resulting in a bulk birefringence grating (BBG). We investigate the process of dual grating formation (SRG and BBG) in a polymer film utilizing a dedicated set-up that combines probe beam diffraction and atomic force microscopy (AFM) measurements, and permits acquiring in situ and in real-time information about changes in local topography and birefringence. We find that the SRG maxima appear at the positions of linearly polarized light (tilted by 45° relative to the grating vector), causing the formation of the half-period topography. This permits to inscribe symmetric and asymmetric topography gratings with sub-wavelength period, while changing only slightly the polarization of one of the interfering beams. We demonstrate an easy generation of sawtooth profiles (blazed gratings) with adjustable shape. With these results, we have taken a significant step in understanding the photo-induced deformation of azo-polymer films.

Effects of Metal Stress on Visible/Near-Infrared Reflectance Spectra of Vegetation

2011 ◽  
Vol 347-353 ◽  
pp. 2735-2738 ◽  
Author(s):  
Guang Yu Chi ◽  
Yi Shi ◽  
Xin Chen ◽  
Jian Ma ◽  
Tai Hui Zheng
Keyword(s):  
Heavy Metal ◽  
Structural Changes ◽  
Near Infrared ◽  
Leaf Surface ◽  
Spectral Response ◽  
Spectral Characteristics ◽  
Surface Characteristics ◽  
Heavy Metal Stress ◽  
Metal Stress ◽  
Near Infrared Reflectance

Vegetation which suffers from heavy metal stresses can cause changes of leaf color, shape and structural changes. The spectral characteristics of vegetation leaves is related to leaf thickness, leaf surface characteristics, the content of water, chlorophyll and other pigments. So the eco-physiology changes of plants can be reflected by spectral reflectance. Studies on the spectral response of vegetation to heavy metal stress can provide a theoretical basis for remote sensing monitoring of metal pollution in soils. In recent decades, there are substantial amounts of literature exploring the effects of heavy metals on vegetation spectra.

A single chip 1/2" frame transfer CCD color image sensor

1984 ◽  
Author(s):  
N. Mitani ◽  
T. Furusawa ◽  
Y. Tsuchihashi ◽  
Y. Kitamura ◽  
Y. Kiriyama ◽  
...  
Keyword(s):  
Color Image ◽  
Image Sensor ◽  
Single Chip
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