Designing Color Filters that Make Cameras MoreColorimetric
- URL: http://arxiv.org/abs/2003.12645v1
- Date: Fri, 27 Mar 2020 21:41:30 GMT
- Title: Designing Color Filters that Make Cameras MoreColorimetric
- Authors: Graham D. Finlayson and Yuteng Zhu
- Abstract summary: In this paper, we solve for the filter which returns the modified sensitivities as close to being a linear transformation from the color matching functions of human visual system as possible.
Experiments demonstrate that, by taking pictures through our optimised color filters we can make cameras significantly more colorimetric.
- Score: 14.097215740999408
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: When we place a colored filter in front of a camera the effective camera
response functions are equal to the given camera spectral sensitivities
multiplied by the filter spectral transmittance. In this paper, we solve for
the filter which returns the modified sensitivities as close to being a linear
transformation from the color matching functions of human visual system as
possible. When this linearity condition - sometimes called the Luther condition
- is approximately met, the `camera+filter' system can be used for accurate
color measurement. Then, we reformulate our filter design optimisation for
making the sensor responses as close to the CIEXYZ tristimulus values as
possible given the knowledge of real measured surfaces and illuminants spectra
data. This data-driven method in turn is extended to incorporate constraints on
the filter (smoothness and bounded transmission). Also, because how the
optimisation is initialised is shown to impact on the performance of the
solved-for filters, a multi-initialisation optimisation is developed.
Experiments demonstrate that, by taking pictures through our optimised color
filters we can make cameras significantly more colorimetric.
Related papers
- Prompt-Guided Image-Adaptive Neural Implicit Lookup Tables for Interpretable Image Enhancement [4.233370898095789]
interpretable image enhancement is a technique that enhances image quality by adjusting filter parameters with easily understandable names such as "Exposure" and "Contrast"
We introduce an image-adaptive neural implicit lookup table, which uses a multilayer perceptron to implicitly define the transformation from input feature space to output color space.
We evaluate visual impressions of enhancement results, such as exposure and contrast, using a vision and language model along with guiding prompts.
arXiv Detail & Related papers (2024-08-20T17:59:01Z) - Motion-adaptive Separable Collaborative Filters for Blind Motion Deblurring [71.60457491155451]
Eliminating image blur produced by various kinds of motion has been a challenging problem.
We propose a novel real-world deblurring filtering model called the Motion-adaptive Separable Collaborative Filter.
Our method provides an effective solution for real-world motion blur removal and achieves state-of-the-art performance.
arXiv Detail & Related papers (2024-04-19T19:44:24Z) - Polarization Multi-Image Synthesis with Birefringent Metasurfaces [3.2428991403246834]
We introduce a new system that uses a birefringent metasurface with a polarizer-mosaicked photosensor to capture four optically-coded measurements in a single exposure.
We apply this system to the task of incoherent opto-electronic filtering, where digital spatial-filtering operations are replaced by simpler, per-pixel sums.
arXiv Detail & Related papers (2023-07-16T17:14:39Z) - Enhancing Low-Light Images Using Infrared-Encoded Images [81.8710581927427]
Previous arts mainly focus on the low-light images captured in the visible spectrum using pixel-wise loss.
We propose a novel approach to increase the visibility of images captured under low-light environments by removing the in-camera infrared (IR) cut-off filter.
arXiv Detail & Related papers (2023-07-09T08:29:19Z) - Reverse image filtering using total derivative approximation and
accelerated gradient descent [82.93345261434943]
We address a new problem of reversing the effect of an image filter, which can be linear or nonlinear.
The assumption is that the algorithm of the filter is unknown and the filter is available as a black box.
We formulate this inverse problem as minimizing a local patch-based cost function and use total derivative to approximate the gradient which is used in gradient descent to solve the problem.
arXiv Detail & Related papers (2021-12-08T05:16:11Z) - Fourier Series Expansion Based Filter Parametrization for Equivariant
Convolutions [73.33133942934018]
2D filter parametrization technique plays an important role when designing equivariant convolutions.
New equivariant convolution method based on the proposed filter parametrization method, named F-Conv.
F-Conv evidently outperforms previous filter parametrization based method in image super-resolution task.
arXiv Detail & Related papers (2021-07-30T10:01:52Z) - Tuning IR-cut Filter for Illumination-aware Spectral Reconstruction from
RGB [84.1657998542458]
It has been proven that the reconstruction accuracy relies heavily on the spectral response of the RGB camera in use.
This paper explores the filter-array based color imaging mechanism of existing RGB cameras, and proposes to design the IR-cut filter properly for improved spectral recovery.
arXiv Detail & Related papers (2021-03-26T19:42:21Z) - Adaptive Debanding Filter [55.42929350861115]
Banding artifacts manifest as staircase-like color bands on pictures or video frames.
We propose a content-adaptive smoothing filtering followed by dithered quantization, as a post-processing module.
Experimental results show that our proposed debanding filter outperforms state-of-the-art false contour removing algorithms both visually and quantitatively.
arXiv Detail & Related papers (2020-09-22T20:44:20Z) - Unifying Optimization Methods for Color Filter Design [3.8073142980733]
Through optimization we can solve for a filter that when the camera views the world through this filter, it is more colorimetric.
Previous work solved for the filter that best satisfied the Luther condition: the camera spectral sensitivities after filtering were approximately a linear transform from the CIE XYZ color matching functions.
A more recent method optimized for the filter that maximized the Vora-Value (a measure which relates to the closeness of the vector spaces spanned by the camera sensors and human vision sensors).
arXiv Detail & Related papers (2020-06-24T11:01:56Z) - Designing a Color Filter via Optimization of Vora-Value for Making a
Camera more Colorimetric [14.097215740999408]
The Luther condition states that if the spectral sensitivity responses of a camera are a linear transform from the color matching functions of the human visual system, the camera is colorimetric.
Previous work proposed to solve for a filter which, when placed in front of a camera, results in sensitivities that best satisfy the Luther condition.
This paper begins with the observation that the cone fundamentals, XYZ color matching functions or any linear combination thereof span the same 3-dimensional subspace.
arXiv Detail & Related papers (2020-05-13T16:51:21Z) - Designing a physically-feasible colour filter to make a camera more
colorimetric [3.8073142980733]
We extend the Luther-condition filter optimisation method to allow us to incorporate the smoothness and transmittance bounds of the recovered filter.
Experiments demonstrate that we can find physically realisable filters which are smooth and reasonably transmissive with which the effective "camera+filter" becomes significantly more colorimetric.
arXiv Detail & Related papers (2020-04-26T19:45:54Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.