An optical model of the appearance of blemishes in digital photographs

Zamfir, Adrian; Drîmbarean, Alexandru; Zamfir, Marta; Buzuloiu, Vasile; Steinberg, E.; Ursu, Daniel C.

doi:10.1117/12.702807

Cited by 7 publications

(5 citation statements)

References 2 publications

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“…Many image processing methods remove blemishes e.g., Zamfir et al [55] detect blemishes and compute their physical appearance (size, shape, position and transparency) in an image as a function of camera settings, while Steinberg et al [56] process images to automatically correct dust.…”

Section: Spotsmentioning

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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Section: Spotsmentioning

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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“…In recent years, several software based dust spot detection and removal schemes have been proposed [21], [22], [23], [24]. These methods usually aim at detecting dust positions from a flat background by examining intensity gradient degradations.…”

Section: A Dust Spot Detectionmentioning

confidence: 99%

Digital Single Lens Reflex Camera Identification From Traces of Sensor Dust

Dirik¹,

Sencar

Memon

2008

IEEE Trans.Inform.Forensic Secur.

152

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Abstract-Digital single lens reflex cameras suffer from a well known sensor dust problem due to interchangeable lenses they deploy. The dust particles that settle in front of the imaging sensor create a persistent pattern in all captured images. In this paper, we propose a novel source camera identification method based on detection and matching of these dust spot characteristics. Dust spots in the image are detected based on a (Gaussian) intensity loss model and shape properties. To prevent false detections, lens parameter dependent characteristics of dust spots are also taken into consideration. Experimental results show that the proposed detection scheme can be used in identification of the source digital single lens reflex (DSLR) camera at low false positive rates, even under heavy compression and downsampling.

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“…methods remove blemishes e.g., Zamfir et al[55] detect blemishes and compute their physical appearance (size, shape, position and transparency) in an image as a function of camera settings, while Steinberg et al[56] process images to automatically correct dust. Banding BAND There are various ways to reduce visual effects of banding e.g., by applying dithering patterns[59].…”

mentioning

confidence: 99%

On failures of RGB cameras and their effects in autonomous driving applications

Secci

Ceccarelli

2020

2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE)

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RGB cameras are arguably 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 failures model 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 the trained agent of an autonomous driving simulator: the misbehavior of the trained agent allows a better understanding of failures effects and especially of the resulting safety risk.

show abstract

An optical model of the appearance of blemishes in digital photographs

Cited by 7 publications

References 2 publications

RGB Cameras Failures and Their Effects in Autonomous Driving Applications

RGB Cameras Failures and Their Effects in Autonomous Driving Applications

Digital Single Lens Reflex Camera Identification From Traces of Sensor Dust

On failures of RGB cameras and their effects in autonomous driving applications

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