Showing papers on "Dark-frame subtraction published in 2023"
TL;DR: In this article , a reset noise reduction method using a feedback amplifier that results in an 80% noise reduction in 3-transistor (3-T) pixels is presented, and the experimental power spectral density indicates potential for further noise cancellation in future devices.
Abstract: A reset noise reduction method using a feedback amplifier that results in an 80% noise reduction in 3-transistor (3-T) pixels is presented. 3-T pixels are useful for non-visible imaging applications because they have fewer post-processing issues than 4-T pixels and do not require charge transfer. They suffer from reset noise because correlated-double sampling cannot be realized without additional memory. Analysis of the experimental power spectral density indicates potential for further noise cancellation in future devices.
TL;DR: In this article , the authors proposed a framework for noise estimation and filtering process to obtain the enhanced images, which is based on noise type and estimation of noise, filter need to be adopted for enhancing the quality of the image.
Abstract: The image enhancement for the natural images is the vast field where the quality of the images degrades based on the capturing and processing methods employed by the capturing devices. Based on noise type and estimation of noise, filter need to be adopted for enhancing the quality of the image. In the same manner, the medical field also needs some filtering mechanism to reduce the noise and detection of the disease based on the clarity of the image captured; in accordance with it, the preprocessing steps play a vital role to reduce the burden on the radiologist to make the decision on presence of disease. Based on the estimated noise and its type, the filters are selected to delete the unwanted signals from the image. Hence, identifying noise types and denoising play an important role in image analysis. The proposed framework addresses the noise estimation and filtering process to obtain the enhanced images. This paper estimates and detects the noise types, namely Gaussian, motion artifacts, Poisson, salt-andpepper, and speckle noises. Noise is estimated by using discrete wavelet transformation (DWT). This separates the image into quadruple sub-bands. Noise and HH sub-band are high-frequency components. HH sub-band also has vertical edges. These vertical edges are removed by performing Hadamard operation on downsampled Sobel edge-detected image and HH sub-band. Using HH sub-band after removing vertical edges is considered for estimating the noise. The Rician energy equation is used to estimate the noise. This is given as input for Artificial Neural Network to improve the estimated noise level. For identifying noise type, CNN is used. After removing vertical edges, the HH sub-band is given to the CNN model for classification. The classification accuracy results of identifying noise type are 100% on natural images and 96.3% on medical images.
TL;DR: In this paper , the authors proposed a method for evaluating digital camera noise, which allows to estimate all four noise components: light and dark fixed-pattern (spatial) and temporal noise.
Abstract: This article proposes a method for evaluating digital camera noise. The method allows to estimate all four noise components: light and dark fixed-pattern (spatial) and temporal noise. We need to register only two scenes: dark and uniform light. Dark spatial and dark temporal noise is measured from two dark frames. Light temporal and light spatial noise is estimated from two light frames using automatic segmentation of a uniform target (ASUT). Noise of visible range (silicon) cameras of four types was measured: consumer Canon EOS M100, machine vision PixeLink PL-B781F, microscope Retiga R6, and scientific MegaPlus II ES11000. Obtained noise values are equal to that ones for the EMVA Standard 1288 within the value spread. The difference is 0.1% (dark temporal), 1.0% (dark spatial), 1.3% (light spatial), and 2.5% (light temporal) only. The proposed method provides a fast and accurate camera noise estimation. This can be used to improve information quality and speed of light registration systems.
08 Jun 2023
TL;DR: In this paper , the authors examined how traditional filtering methods restore a digital image under salt and pepper and Gaussian noise interference, and the effects of filtered noise pictures were evaluated through extensive experimental simulations, providing substantial assistance for related research.
Abstract: Due to interference in the transmission of external equipment, images can suffer from varying noise concentrations. As an immediate and practical step to reduce the impact of noise on an image and to improve its quality and visual presentation, filtering is the best method. The paper examines how traditional filtering methods restore a digital image under salt and pepper and Gaussian noise interference. Also included in this paper is an analysis of the advantages and challenges of traditional noise filtering, as well as suggestions for a filtering method for a variety of noise levels. Lastly, the effects of filtered noise pictures are evaluated through extensive experimental simulations, providing substantial assistance for related research.
TL;DR: In this paper , the authors discuss Gaussian noise, salt-and-pepper noise, speckle noise, and filters including Mean, Median, and Gaussianfilters, which can be used to remove noise from an image.
Abstract: In an ever-evolving world, countless fresh methods for reflecting data are being developed. An image is used to illustrate. Compound images and data are widely used. Numerous photos that have been acquired contain noise, which impairs the image's qualities and produces visual disruption. Noise should be eliminated in order to preserve good image quality, as this helps the image restore its quality. Applying filters like the Median, Wiener, Gaussian, etc. can be used to remove noise from an image. This essay discusses Gaussian noise, salt-and-pepper noise, speckle noise, and filters including Mean, Median, and Gaussianfilters.
04 Apr 2023
TL;DR: In this article , the authors studied the noise characteristics of EBAPS and proposed a noise suppression method to obtain a high SNR digital output image under different operating modes, including photoelectric conversion, gain and readout.
Abstract: Electron Bombardment Active Pixel Sensor (EBAPS) can work in photosensitive mode and electrical sensitive mode due to the special doping mode of CMOS. In both operating modes, after the target signal passes through the photoelectric conversion, gain and readout process of the EBAPS device, the readout signal needs to exceed the noise generated by the device to ensure the distinguishable output image. However, in the process of conversion and multiplication of the target signal, noise will inevitably be introduced. The noise will be amplified along with the signal, causing distortion or attenuation of the original signal, thus interfering with the quality of the output image and affecting human observation. Therefore, it is necessary to study the noise characteristics of EBAPS as a key factor affecting the imaging quality. For the development of high-performance EBAPS devices, this paper focuses on the noise characteristics of detection and imaging under different operating modes. By analyzing the working principle of EBAPS devices in different working modes, the noise sources that affect the imaging quality are obtained. In photosensitive mode, the noise of EBAPS is consistent with that of ordinary CMOS image sensor. These noises are mainly affected by CMOS process level, ambient temperature, working time and other factors, and can usually be removed by image processing algorithms. In the electric sensitive mode, the noise of EBAPS mainly comes from GaAs photocathode and the electron multiplication process of CMOS. These noises can be suppressed by reducing the working temperature, improving the surface defects and cleanliness during the chip preparation, and improving the doping process of the substrate. According to the noise generation mechanism, the noise suppression methods are proposed to obtain a high SNR digital output image. The above research provides some references for the following research on noise characteristics and noise reduction methods of digital low light level devices.