Single-Image Camera Response Function Using Prediction Consistency and Gradual Refinement

Sharma, Aashish; Tan, Robby T.; Cheong, Loong-Fah

doi:10.1007/978-3-030-69544-6_2

Cited by 6 publications

(3 citation statements)

References 25 publications

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“…Such methods work by analysing histogram [38], geometry invariants [39], and colour blending [40] in edge regions, and by using probabilistic intensity similarity [41] and noise distribution in each image [42]. Deep learning has also been utilised by numeral recent works [43], [44].…”

Section: B Camera Response Calibrationmentioning

confidence: 99%

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Color Alignment for Relative Color Constancy via Non-Standard References

Zhao

Ferguson

Zhou

et al. 2022

IEEE Trans. on Image Process.

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Relative colour constancy is an essential requirement for many scientific imaging applications. However, most digital cameras differ in their image formations and native sensor output is usually inaccessible, e.g., in smartphone camera applications. This makes it hard to achieve consistent colour assessment across a range of devices, and that undermines the performance of computer vision algorithms. To resolve this issue, we propose a colour alignment model that considers the camera image formation as a black-box and formulates colour alignment as a threestep process: camera response calibration, response linearisation, and colour matching. The proposed model works with nonstandard colour references, i.e., colour patches without knowing the true colour values, by utilising a novel balance-of-lineardistances feature. It is equivalent to determining the camera parameters through an unsupervised process. It also works with a minimum number of corresponding colour patches across the images to be colour aligned to deliver the applicable processing. Three challenging image datasets collected by multiple cameras under various illumination and exposure conditions, including one that imitates uncommon scenes such as scientific imaging, were used to evaluate the model. Performance benchmarks demonstrated that our model achieved superior performance compared to other popular and state-of-the-art methods.

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Section: B Camera Response Calibrationmentioning

confidence: 99%

“…The root-mean-square error (RMSE) [21], [40], [43], [44], [46] recovery angular error (RAE) [18], [20], [21], [52], [53] and E 2000 [22], [29] are common metrics to quantify colour difference. The RMSE is an ideal metric for quantifying colour intensity difference as it measures the Euclidean distance between two compared CCPs.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Color Alignment for Relative Color Constancy via Non-Standard References

Zhao

Ferguson

Zhou

et al. 2022

IEEE Trans. on Image Process.

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show abstract

“…The root-mean-square error (RMSE) [2,8,[25][26][27] has been widely used to quantify colour difference. It measures the Euclidean distance between two compared vectors:…”

Section: Evaluation Metricsmentioning

confidence: 99%

Representing Camera Response Function by a Single Latent Variable and Fully Connected Neural Network

Zhao

Ferguson

Zhou

et al. 2022

SIViP

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Modelling the mapping from scene irradiance to image intensity is essential for many computer vision tasks. Such mapping is known as the camera response. Most digital cameras use a nonlinear function to map irradiance, as measured by the sensor to an image intensity used to record the photograph. Modelling of the response is necessary for the nonlinear calibration. In this paper, a new high-performance camera response model that uses a single latent variable and fully connected neural network is proposed. The model is produced using unsupervised learning with an autoencoder on real-world (example) camera responses. Neural architecture searching is then used to find the optimal neural network architecture. A latent distribution learning approach was introduced to constrain the latent distribution. The proposed model achieved state-of-the-art CRF representation accuracy in a number of benchmark tests, but is over twice as fast as the best current models when performing the maximum likelihood estimation during camera response calibration due to the simple yet efficient model representation.

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