CMOS circuits for high-performance imaging

Choubey, Bhaskar; Mughal, Waqas; Gouveia, Luiz Carlos

doi:10.1016/b978-0-08-102434-8.00005-2

Cited by 2 publications

(2 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A key goal of image analysis is to automatically extract information contained in an image using a suitable algorithm [1]. The devices used for image acquisition are usually based on either charge-coupled device (CCD) sensors [2] or complementary metal-oxide-semiconductor (CMOS) sensors [3]. Although the specific properties of recording techniques differ, all types induce artifacts caused by the process of capturing images [4].…”

Section: Introductionmentioning

confidence: 99%

A Data-Centric Augmentation Approach for Disturbed Sensor Image Segmentation

2021

View full text Add to dashboard Cite

Deep learning methods have become increasingly popular for optical sensor image analysis. They are adaptable to specific tasks and simultaneously demonstrate a high degree of generalization capability. However, applying deep neural networks to problems with low availability of labeled training data can lead to a model being incapable of generalizing to possible scenarios that may occur in test data, especially with the occurrence of dominant imaging artifacts. We propose a data-centric augmentation approach based on generative adversarial networks that overlays the existing labeled data with synthetic artifacts that are generated from data not present in the training set. This augmentation leads to a more robust generalization capability in semantic segmentation. Our method does not need any additional labeling and does not lead to additional memory or time consumption during inference. Further, we find it to be more effective than comparable approaches that are based on procedurally generated disturbances and the direct use of real disturbances. Building upon the improved segmentation results, we observe that our approach leads to improvements of 22% in the F1-score for an evaluated detection problem, which promises significant robustness towards future disturbances. In the context of sensor-based data analysis, the compensation of image artifacts is a challenge. When the structures of interest are not clearly visible in an image, algorithms that can cope with artifacts are crucial for obtaining the desired information. Thereby, the high variation of artifacts, the combination of different types of artifacts, and their similarity to signals of interest are specific issues that have to be considered in the analysis. Despite the high generalization capability of deep learning-based approaches, their recent success was driven by the availability of large amounts of labeled data. Therefore, the provision of comprehensive labeled image data with different characteristics of image artifacts is of importance. At the same time, applying deep neural networks to problems with low availability of labeled data remains a challenge. This work presents a data-centric augmentation approach based on generative adversarial networks that augments the existing labeled data with synthetic artifacts generated from data not present in the training set. In our experiments, this augmentation leads to a more robust generalization in segmentation. Our method does not need additional labeling and does not lead to additional memory or time consumption during inference. Further, we find it to be more effective than comparable augmentations based on procedurally generated artifacts and the direct use of real artifacts. Building upon the improved segmentation results, we observe that our approach leads to improvements of 22% in the F1-score for an evaluated detection problem. Having achieved these results with an example sensor, we expect increased robustness against artifacts in future applications.

show abstract

Section: Introductionmentioning

confidence: 99%

A Data-Centric Augmentation Approach for Disturbed Sensor Image Segmentation

2021

View full text Add to dashboard Cite

show abstract

“…1a). However, owing to the column-parallel analog-digital converter architecture typical of CMOS sensors 27,28 , framerates are dependent on the number of pixel rows rather than the total number of pixels. As a result, the pursuit of higher framerates with this configuration may lead to an undesirably narrow FOV, obstructing the visualization of wide-field, isotropic biological features, even at low magnification.…”

mentioning

confidence: 99%

High-speed optical imaging with sCMOS pixel reassignment

Mandracchia,

Zheng,

Rajendran

et al. 2024

Nat Commun

View full text Add to dashboard Cite

Fluorescence microscopy has undergone rapid advancements, offering unprecedented visualization of biological events and shedding light on the intricate mechanisms governing living organisms. However, the exploration of rapid biological dynamics still poses a significant challenge due to the limitations of current digital camera architectures and the inherent compromise between imaging speed and other capabilities. Here, we introduce sHAPR, a high-speed acquisition technique that leverages the operating principles of sCMOS cameras to capture fast cellular and subcellular processes. sHAPR harnesses custom fiber optics to convert microscopy images into one-dimensional recordings, enabling acquisition at the maximum camera readout rate, typically between 25 and 250 kHz. We have demonstrated the utility of sHAPR with a variety of phantom and dynamic systems, including high-throughput flow cytometry, cardiomyocyte contraction, and neuronal calcium waves, using a standard epi-fluorescence microscope. sHAPR is highly adaptable and can be integrated into existing microscopy systems without requiring extensive platform modifications. This method pushes the boundaries of current fluorescence imaging capabilities, opening up new avenues for investigating high-speed biological phenomena.

show abstract

CMOS circuits for high-performance imaging

Cited by 2 publications

References 65 publications

A Data-Centric Augmentation Approach for Disturbed Sensor Image Segmentation

A Data-Centric Augmentation Approach for Disturbed Sensor Image Segmentation

High-speed optical imaging with sCMOS pixel reassignment

Contact Info

Product

Resources

About