Real-Time Photo Sensor Dead Pixel Detection for Embedded Devices

Cho, Chao-Yi; Chen, Tse-Min; Wang, Wen-Shan; Liu, Chun-Nan

doi:10.1109/dicta.2011.34

Cited by 16 publications

(16 citation statements)

References 4 publications

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“…The IPC transforms a raw image data from the CMOS sensor into a full-color image [ 30 ]. The IPC includes many image processing blocks such as dead pixel correction [ 31 ], noise reduction [ 32 ], white balance [ 33 ], demosaic [ 34 ], etc. A video encoder converts the full color images to a compressed video bitstream.…”

Section: Proposed Methods For Reducing Memory Access Amountmentioning

confidence: 99%

Low Memory Access Video Stabilization for Low-Cost Camera SoC

Lee

2022

Sensors

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Video stabilization is one of the most important features in consumer cameras. Even simple video stabilization algorithms may need to access the frames several times to generate a stabilized output image, which places a significant burden on the camera hardware. This high-memory-access requirement makes it difficult to implement video stabilization in real time on low-cost camera SoC. Reduction of the memory usage is a critical issue in camera hardware. This paper presents a structure and layout method to efficiently implement video stabilization for low-end hardware devices in terms of shared memory access amount. The proposed method places sub-components of video stabilization in a parasitic form in other processing blocks, and the sub-components reuse data read from other processing blocks without directly accessing data in the shared memory. Although the proposed method is not superior to the state-of-the-art methods applied in post-processing in terms of video quality, it provides sufficient performance to lower the cost of camera hardware for the development of real-time devices. According to my analysis, the proposed one reduces the memory access amount by 21.1 times compared to the straightforward method.

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Section: Proposed Methods For Reducing Memory Access Amountmentioning

confidence: 99%

Low Memory Access Video Stabilization for Low-Cost Camera SoC

Lee

2022

Sensors

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“…It is possible to detect dead pixels with solutions implementable directly in the embedded device [57]; circuits to correct dead pixels can be fabricated on a single integrated chip [58].…”

Section: Deapixmentioning

confidence: 99%

RGB Cameras Failures and Their Effects in Autonomous Driving Applications

Secci

Ceccarelli

2023

IEEE Trans. Dependable and Secure Comput.

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RGB cameras are one of the most relevant sensors for autonomous driving applications. It is undeniable that failures of vehicle cameras may compromise the autonomous driving task, possibly leading to unsafe behaviors when images that are subsequently processed by the driving system are altered. To support the definition of safe and robust vehicle architectures and intelligent systems, in this paper we define the failure modes of a vehicle camera, together with an analysis of effects and known mitigations. Further, we build a software library for the generation of the corresponding failed images, and we feed them to six object detectors for mono and stereo cameras and to the self-driving agent of an autonomous driving simulator. The resulting misbehaviors with respect to operating with clean images allow a better understanding of failures effects and the related safety risks in image-based applications.

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“…There exist three different types of defective pixels: hot, dead and weak. A hot pixel is a pixel with the highest arithmetic value and a dead pixel is a pixel with the lowest value [19] [20]. A hot pixel can be seen as a pixel stuck at '1' and a dead pixel can be seen as a pixel stuck at '0'.…”

Section: Defective Pixel Definition and Determinationmentioning

confidence: 99%