LIP operators: Simulating exposure variations to perform algorithms independent of lighting conditions

Carré, Maxime; Jourlin, Michel

doi:10.1109/icmcs.2014.6911247

Cited by 9 publications

(7 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Images acquired under very short exposure times and/or degraded lighting conditions need a preprocessing step to get a reliable stabilization of their brightness. Previous works [ 22 , 23 ] have demonstrated the effectiveness of LIP tools to perform such a brightness correction, even in extreme conditions of very low-light images. Compared to classical enhancement methods, the LIP approach presents two major advantages: it permits precise modeling of variable exposure times and has been demonstrated to be consistent with human vision [ 10 ].…”

Section: Discussionmentioning

confidence: 99%

“…In the majority of the aforementioned methods, the enhancement step was not driven by a rigorous goal but aimed at making low-light images visually interpretable. To improve such an approach, Carré established that it was possible to perfectly simulate variations of exposure time by performing LIP addition/subtraction of a constant [ 22 , 23 ]. This applies to grey level images (cf.…”

Section: Exposure Time Simulationmentioning

confidence: 99%

See 1 more Smart Citation

Extending Camera’s Capabilities in Low Light Conditions Based on LIP Enhancement Coupled with CNN Denoising

Carré¹,

Jourlin

2021

Sensors

Self Cite

View full text Add to dashboard Cite

Using a sensor in variable lighting conditions, especially very low-light conditions, requires the application of image enhancement followed by denoising to retrieve correct information. The limits of such a process are explored in the present paper, with the objective of preserving the quality of enhanced images. The LIP (Logarithmic Image Processing) framework was initially created to process images acquired in transmission. The compatibility of this framework with the human visual system makes possible its application to images acquired in reflection. Previous works have established the ability of the LIP laws to perform a precise simulation of exposure time variation. Such a simulation permits the enhancement of low-light images, but a denoising step is required, realized by using a CNN (Convolutional Neural Network). A main contribution of the paper consists of using rigorous tools (metrics) to estimate the enhancement reliability in terms of noise reduction, visual image quality, and color preservation. Thanks to these tools, it has been established that the standard exposure time can be significantly reduced, which considerably enlarges the use of a given sensor. Moreover, the contribution of the LIP enhancement and denoising step are evaluated separately.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Exposure Time Simulationmentioning

confidence: 99%

Extending Camera’s Capabilities in Low Light Conditions Based on LIP Enhancement Coupled with CNN Denoising

Carré¹,

Jourlin

2021

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Knowing that the addition of a constant to a function is equivalent to a variation of camera exposure-time (Carre and Jourlin, 2014;Deshayes et al, 2015), we have the fundamental result: the LIP-additive Asplund metric is insensitive to exposure-time changing.…”

Section: Lip-additive Asplund Metricmentioning

confidence: 99%

“…-The addition (or subtraction) of a constant c to (or from) f simulates the decrease (or increase) of the acquisition exposure-time (Carre and Jourlin, 2014;Deshayes et al, 2015). If the values of f − − − c become strictly negative, they behave as light intensifiers (Jourlin, 2016, chap.…”

Section: Logarithmic Image Processingmentioning

confidence: 99%

Functional Asplund metrics for pattern matching, robust to variable lighting conditions

Noyel

Jourlin

2020

Image Anal Stereol

Self Cite

View full text Add to dashboard Cite

In this paper, we propose a complete framework to process images captured under uncontrolled lighting and especially under low lighting. By taking advantage of the Logarithmic Image Processing (LIP) context, we study two novel functional metrics: i) the LIP-multiplicative Asplund metric which is robust to object absorption variations and ii) the LIP-additive Asplund metric which is robust to variations of source intensity or camera exposure-time. We introduce robust to noise versions of these metrics. We demonstrate that the maps of their corresponding distances between an image and a reference template are linked to Mathematical Morphology. This facilitates their implementation. We assess them in various situations with different lightings and movement. Results show that those maps of distances are robust to lighting variations. Importantly, they are efficient to detect patterns in low-contrast images with a template acquired under a different lighting.

show abstract

“…• The addition (or subtraction) of a constant c to (or from) f simulates the decrease (or increase) of the acquisition exposure-time [44,45]. If the values of f − c become strictly negative, they perform as light intensifiers [27, chap.…”

Section: Logarithmic Image Processingmentioning

confidence: 99%

Functional Asplund metrics for pattern matching, robust to variable lighting conditions

Noyel,

Jourlin

2019

Preprint

Self Cite

View full text Add to dashboard Cite

In this paper, we propose a complete framework to process images captured under uncontrolled lighting and especially under low lighting. By taking advantage of the Logarithmic Image Processing (LIP) context, we study two novel functional metrics: i) the LIP-multiplicative Asplund's metric which is robust to object absorption variations and ii) the LIP-additive Asplund's metric which is robust to variations of source intensity and exposure-time. We introduce robust to noise versions of these metrics. We demonstrate that the maps of their corresponding distances between an image and a reference template are linked to Mathematical Morphology. This facilitates their implementation. We assess them in various situations with different lightings and movements. Results show that those maps of distances are robust to lighting variations. Importantly, they are efficient to detect patterns in low-contrast images with a template acquired under a different lighting. 1

show abstract

LIP operators: Simulating exposure variations to perform algorithms independent of lighting conditions

Cited by 9 publications

References 7 publications

Extending Camera’s Capabilities in Low Light Conditions Based on LIP Enhancement Coupled with CNN Denoising

Extending Camera’s Capabilities in Low Light Conditions Based on LIP Enhancement Coupled with CNN Denoising

Functional Asplund metrics for pattern matching, robust to variable lighting conditions

Functional Asplund metrics for pattern matching, robust to variable lighting conditions

Contact Info

Product

Resources

About