Dangers of Demosaicing: Confusion From Correlation

Eskelinen, Matti A.; Hämäläinen, Jyri

doi:10.1109/whispers.2018.8747204

Cited by 2 publications

(2 citation statements)

References 6 publications

(7 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, lesser work has investigated their use for radiance data analysis. Still, the bilinear, Malvar and Menon algorithms have recently been compared in the context of hyperspectral spectroscopy (Eskelinen and Hämäläinen 2018). In that context, the use of the advanced methods (Malvar and Menon algorithms) resulted in an overall error increase of 9%-14% compared to the bilinear results.…”

Section: Resultsmentioning

confidence: 99%

On the use of polychromatic cameras for high spatial resolution spectral dose measurements

2022

View full text Add to dashboard Cite

Objective: Despite the demonstrated benefits of hyperspectral formalism for stem effect corrections in the context of fiber dose measurements, this approach has not been yet translated into volumetric measurements where cameras are typically used for their distinguishing spatial resolution. This work investigates demosaicing algorithms for polychromatic cameras based spectral imaging. Approach: The scintillation and Cherenkov signals produced in a radioluminescent phantom are imaged by a polychromatic camera and isolated using the spectral formalism. To do so, five demosaicing algorithms are investigated from calibration to measurements: a clustering method and four interpolation algorithms. The resulting accuracy of scintillation and Cherenkov images is evaluated with measurements of the differences (mean ± standard deviation) between the obtained and expected signals from profiles drawn across a scintillation spot. Signal-to-noise ratio and signal-to-background ratio are further measured and compared in the resulting scintillation images. Finally, the resulting differences on the scintillation signal from a 0.2x0.2 cm^2 region-of-interest (ROI) were reported. Main results: Clustering, OpenCV, bilinear, Malvar and Menon demosaicing algorithms respectively yielded differences of 3±5%, 1±3%, 1±3%, 1±2% and 2±4% in the resulting scintillation images. For the Cherenkov images, all algorithms provided differences below 1%. All methods enabled measurements over the detectability (SBR>2) and sensitivity (SNR>5) thresholds with the bilinear algorithm providing the best SNR value. Clustering, OpenCV, bilinear, Malvar and Menon demosaicing algorithms respectively provided differences on the ROI analysis of 7±5%, 3±2%, 3±2%, 4±2%, 7±3%. Significance Radioluminescent signals can accurately be isolated using a single polychromatic camera. Moreover, demosaicing using a bilinear kernel provided the best results and enabled Cherenkov signal subtraction while preserving the full spatial resolution of the camera.

show abstract

Section: Resultsmentioning

confidence: 99%

On the use of polychromatic cameras for high spatial resolution spectral dose measurements

2022

View full text Add to dashboard Cite

show abstract

“…Furthermore, due to the nature of the interpolation process, the demosaicing step may lead to errors in the form of erroneously estimated colors, or an artificial jagged pattern known as the zipper effect [16], [17].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Viability of Bypassing the Image Signal Processor for CNN-Based Object Detection in Autonomous Vehicles

et al. 2023

View full text Add to dashboard Cite

In the field of autonomous driving, cameras are crucial sensors for providing information about a vehicle's environment. Image quality refers to a camera system's ability to capture, process, and display signals to form an image. Historically, ''good quality'' in this context refers to images that have been processed by an Image Signal Processor (ISP) designed with the goal of providing the optimal experience for human consumption. However, image quality perceived by humans may not always result in optimal conditions for computer vision. In the context of human consumption, image quality is well documented and understood. Image quality for computer vision applications, such as those in the autonomous vehicle industry, requires more research. Fully autonomous vehicles inevitably encounter constraints concerning data storage, transmission speed, and energy consumption. This is a result of enormous amounts of data being generated by the vehicle from suites made up of multiple different sensors. We propose a potential optimization along the computer vision pipeline, by completely bypassing the ISP block for a class of applications. We demonstrate that doing so has a negligible impact on the performance of Convolutional Neural Network (CNN) object detectors. The results also highlight the benefits of using raw pre-ISP data, in the context of computation and energy savings achieved by removing the ISP.INDEX TERMS Object detection, image signal processor, autonomous vehicles, neural networks, raw data, Bayer filter.

show abstract

Dangers of Demosaicing: Confusion From Correlation

Cited by 2 publications

References 6 publications

On the use of polychromatic cameras for high spatial resolution spectral dose measurements

On the use of polychromatic cameras for high spatial resolution spectral dose measurements

Exploring the Viability of Bypassing the Image Signal Processor for CNN-Based Object Detection in Autonomous Vehicles

Contact Info

Product

Resources

About