A survey on defect and noise detection and correction algorithms in image sensors

Jain, Abhishek; Gupta, Richa

doi:10.1109/icacea.2015.7164803

Cited by 4 publications

(5 citation statements)

References 7 publications

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“…However, the standard for distinguishing abnormal from normal performance is usually not clear. Generally, there are two types of defect pixels: high dark noise pixels and low gain pixels [20, 21]. High dark noise pixels are pixels with high dark electric signal or dark noise variation, including but not limit to high dark current pixels (also called hot pixels).…”

Section: Theory and Methodsmentioning

confidence: 99%

“…Clearly, defect pixels result in high noise non-uniformity. Note that the number of defect pixels usually increases during the manufacturing process or during the usage of an sCMOS camera [20, 21]. Besides, since defect pixels (or high noise pixels) are easily observed in enlarge images, they were often used to analyze the impact of noise non-uniformity on imaging quality [11, 13, 16, 17].…”

Section: Theory and Methodsmentioning

confidence: 99%

“…In the static correction algorithm, special images (typically under homogeneous illumination [20, 23]) are taken and used to calculate the characteristics of the camera noises. In the dynamic correction algorithm, the camera noises are characterized from normally images [20, 21, 23, 24]. Then, different noise correction strategies are used according to the camera noise characteristics.…”

Section: Theory and Methodsmentioning

confidence: 99%

“…For defect pixel correction, a common strategy used in both dynamic and static correction algorithms is that the raw digital value of a defect pixel is substituted by a mean value calculated from the surrounding pixels [21, 26]. In this work, we first determine the defect pixels from their statistical noise characteristics, and then substitute their values by the mean values of the surrounding 8 pixels.…”

Section: Theory and Methodsmentioning

confidence: 99%

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Characterizing and correcting camera noises in back-illuminated sCMOS cameras

Zhang

Wang

Piestun

et al. 2021

Preprint

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With promising properties of fast imaging speed, large field-of-view, relative low cost and many others, back-illuminated sCMOS cameras have been receiving intensive attentions for low-light imaging in the past several years. However, due to the pixel-to-pixel difference of camera noises (called noise non-uniformity) in sCMOS cameras, researchers may hesitate to use them in some application fields, and sometimes wonder whether they should optimize the noise non-uniformity of their sCMOS cameras before using them in a specific application scenario. In this paper, we systematically characterize the impact of different types of sCMOS noises on image quality and perform corrections to these sCMOS noises. We verify that it is possible to use appropriate correction methods to push the non-uniformity of major camera noises, including readout noise, offset, and photon response, to a satisfactory level for conventional microscopy and single molecule localization microscopy. We further find out that, after these corrections, global read noise becomes a major concern that limits the imaging performance of back-illuminated sCMOS cameras. We believe this study provides new insights into the understanding of camera noises in back-illuminated sCMOS cameras, and also provides useful information for future development of this promising camera technology.

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Section: Theory and Methodsmentioning

confidence: 99%

Section: Theory and Methodsmentioning

confidence: 99%

Section: Theory and Methodsmentioning

confidence: 99%

Section: Theory and Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Characterizing and correcting camera noises in back-illuminated sCMOS cameras

Zhang

Wang

Piestun

et al. 2021

Preprint

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“…Post-processing consisting of two steps, cropping and image enhancement, was applied to the output of the composite image generator. Most composite images produced by the ICE included black background areas, which were redundant and removed after blurring with a Gaussian filter [23], gray level thresholding [24] and foreground detection on the thresholded, binary image.…”

Section: The Proposed Methodologymentioning

confidence: 99%

SelectStitch: Automated Frame Segmentation and Stitching to Create Composite Images from Otoscope Video Clips

Binol

Moberly

Niazi

et al. 2020

Preprint

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Background and Objective: The aim of this study is to develop and validate an automated image segmentation-based frame selection and stitching framework to create enhanced composite images from otoscope videos. The proposed framework, called SelectStitch, is useful for classifying eardrum abnormalities using a single composite image instead of the entire raw otoscope video dataset. Methods: SelectStitch consists of a convolutional neural network (CNN) based semantic segmentation approach to detect the eardrum in each frame of the otoscope video, and a stitching engine to generate a high-quality composite image from the detected eardrum regions. In this study, we utilize two separate datasets: the first one has 36 otoscope videos that were used to train a semantic segmentation model, and the second one, containing 100 videos, which was used to test the proposed method. Cases from both adult and pediatric patients were used in this study. A configuration of 4-levels depth U-Net architecture was trained to automatically find eardrum regions in each otoscope video frame from the first dataset. After the segmentation, we automatically selected meaningful frames from otoscope videos by using a pre-defined threshold, i.e., it should contain at least an eardrum region of 20% of a frame size. We have generated 100 composite images from the test dataset. Three ear, nose, and throat (ENT) specialists (ENT-I, ENT-II, ENT-III) compared in two rounds the composite images produced by SelectStitch against the composite images that were generated by the base processes, i.e., stitching all the frames from the same video data, in terms of their diagnostic capabilities. Results: In the first round of the study, ENT-I, ENT-II, ENT-III graded improvement for 58, 57, and 71 composite images out of 100, respectively, for SelectStitch over the base composite, reflecting greater diagnostic capabilities. In the repeat assessment, these numbers were 56, 56, and 64, respectively. We observed that only 6%, 3%, and 3% of the cases received a lesser score than the base composite images, respectively, for ENT-I, ENT-II, and ENT-III in Round-1, and 4%, 0%, and 2% of the cases in Round-2. Conclusions: Frame selection improves the diagnostic quality of composite images from otoscope video clips.

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A Framework for Super Resolution of Color Images with Missing Samples Using Low Rank Approximation

Rahiman

Gerorge

2022

Sens Imaging

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A survey on defect and noise detection and correction algorithms in image sensors

Cited by 4 publications

References 7 publications

Characterizing and correcting camera noises in back-illuminated sCMOS cameras

Characterizing and correcting camera noises in back-illuminated sCMOS cameras

SelectStitch: Automated Frame Segmentation and Stitching to Create Composite Images from Otoscope Video Clips

A Framework for Super Resolution of Color Images with Missing Samples Using Low Rank Approximation

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