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 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.

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

Image Acquisition System Design of Camera Based on FPGA

2014 ◽  
Vol 668-669 ◽  
pp. 836-839
Author(s):  
Jun Chao Zhu ◽  
Yong Chen Li ◽  
Ying Kui Jiao ◽  
Zhi Jun Ma
Keyword(s):  
High Speed ◽  
Image Acquisition ◽  
Image Sensor ◽  
Acquisition System ◽  
Ping Pong ◽  
Cmos Sensor ◽  
Image Acquisition System ◽  
High Speed Data ◽  
Fpga Chip ◽  
Image Collection

It designs an image acquisition system of the camera based on FPGA. It uses a CMOS image sensor as the sensitive chip and controls the timing of image collection by designing the FPGA. FPGA transfers captured image into a PC to display. It uses the I2C bus to initiate CMOS sensor. A problem of cross-clock is solved by asynchronous FIFO. By the ping-pong operation based on two SDRAM chips to solve the problem of high speed data cache. The FPGA chip communicates signal data with PC by Ethernet port. The experiment proved that the system is able to collect 2048×1536 resolution images in a speed of 12fps.

CMOS Real-Time Image Acquisition and Display System Design Based on FPGA

2014 ◽  
Vol 644-650 ◽  
pp. 4403-4406
Author(s):  
Jian Wei Leng ◽  
Ying Hui Wu
Keyword(s):  
Data Acquisition ◽  
System Design ◽  
Real Time ◽  
High Speed ◽  
Image Acquisition ◽  
Control Unit ◽  
Image Sensor ◽  
Acquisition System ◽  
Sensor Data ◽  
Time Data

Based on characteristics of image acquisition system of high-speed and large-capacity, this paper presents a CMOS Image sensor data acquisition system that is using FPGA Chip as its core processing devices. Data acquisition logic control unit is designed by FPGA. The modular structure of the system design, FIFO, ping-pong and other technology are used in the design process to ensure real-time data acquisition and transmission. FPGA implementation of video acquisition can improve system performance. It also has a strong adaptability and flexibility, and it is easy to design, debug and so on. Through the experiment, we can get a clear image.

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.

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