Paper: An improved optimization method for finding a color filter to make a camera more colorimetric

2020 ◽  
Vol 2020 (15) ◽  
pp. 163-1-163-6 ◽  
Author(s):  
Graham D. Finlayson ◽  
Yuteng Zhu
Keyword(s):  
Imaging System ◽  
Optimization Method ◽  
Color Filter ◽  
Simple Extension ◽  
Spectral Transmittance ◽  
Iterative Optimization

Recently, an iterative optimization method was proposed that determines the spectral transmittance of a color filter which, when placed in front of a camera, makes the camera more colorimetric [1]. However, the performance of this method depends strongly on the filter (guess) that initializes the optimization. In this paper, we develop a simple extension to the optimization where we systematically sample the set of possible initial filters and for each initialization solve for the best refinement. Experiments demonstrate that improving the initialization step can result in the effective ‘camera+filter’ imaging system being much more colorimetric. Moreover, the filters we design are smoother than previously reported (which makes them easier to manufacture).

Designing a Color Filter with High Overall Transmittance for Improving the Color Accuracy of Digital Cameras

2021 ◽  
Vol 2021 (29) ◽  
pp. 1-6
Author(s):  
Yuteng Zhu ◽  
Graham D. Finlayson
Keyword(s):  
White Light ◽  
Digital Camera ◽  
Color Filter ◽  
Digital Cameras ◽  
Spectral Transmittance ◽  
Optimal Filters ◽  
Prior Art ◽  
Average Transmittance ◽  
Tristimulus Values ◽  
Correction Problem

Previously improved color accuracy of a given digital camera was achieved by carefully designing the spectral transmittance of a color filter to be placed in front of the camera. Specifically, the filter is designed in a way that the spectral sensitivities of the camera after filtering are approximately linearly related to the color matching functions (or tristimulus values) of the human visual system. To avoid filters that absorbed too much light, the optimization could incorporate a minimum per wavelength transmittance constraint. In this paper, we change the optimization so that the overall filter transmittance is bounded, i.e. we solve for the filter that (for a uniform white light) transmits (say) 50% of the light. Experiments demonstrate that these filters continue to solve the color correction problem (they make cameras much more colorimetric). Significantly, the optimal filters by restraining the average transmittance can deliver a further 10% improvement in terms of color accuracy compared to the prior art of bounding the low transmittance.

Diffuse Incident Spectral Transmittance Evaluation of Filter Based on Full-color Filter Match

2013 ◽  
Vol 42 (9) ◽  
pp. 1097-1101
Author(s):  
袁琨 YUAN Kun ◽  
严惠民 YAN Hui-min ◽  
王聪 WANG Cong
Keyword(s):  
Color Filter ◽  
Spectral Transmittance ◽  
Full Color

scholarly journals Scaling-Based Two-Step Reconstruction in Full Polarization-Compressed Hyperspectral Imaging

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7120
Author(s):  
Axin Fan ◽  
Tingfa Xu ◽  
Xi Wang ◽  
Chang Xu ◽  
Yuhan Zhang
Keyword(s):  
Compressive Sensing ◽  
Hyperspectral Imaging ◽  
Imaging System ◽  
Optimization Method ◽  
Physicochemical Characteristics ◽  
Tunable Filter ◽  
Stokes Parameters ◽  
Quarter Wave Plate ◽  
Reconstruction Method ◽  
Order Of Magnitude

Polarized hyperspectral images can reflect the rich physicochemical characteristics of targets. Meanwhile, the contained plentiful information also brings great challenges to signal processing. Although compressive sensing theory provides a good idea for image processing, the simplified compression imaging system has difficulty in reconstructing full polarization information. Focused on this problem, we propose a two-step reconstruction method to handle polarization characteristics of different scales progressively. This paper uses a quarter-wave plate and a liquid crystal tunable filter to achieve full polarization compression and hyperspectral imaging. According to their numerical features, the Stokes parameters and their modulation coefficients are simultaneously scaled. The first Stokes parameter is reconstructed in the first step based on compressive sensing. Then, the last three Stokes parameters with similar order of magnitude are reconstructed in the second step based on previous results. The simulation results show that the two-step reconstruction method improves the reconstruction accuracy by 7.6 dB for the parameters that failed to be reconstructed by the non-optimized method, and reduces the reconstruction time by 8.25 h without losing the high accuracy obtained by the current optimization method. This feature scaling method provides a reference for the fast and high-quality reconstruction of physical quantities with obvious numerical differences.

ITERATIVE OPTIMIZATION METHOD OF GOB-VOLTERRA FILTERS

2005 ◽  
Vol 38 (1) ◽  
pp. 773-778 ◽  
Author(s):  
Alain Y. Kibangou ◽  
Gérard Favier ◽  
Moha M. Hassani
Keyword(s):  
Optimization Method ◽  
Volterra Filters ◽  
Iterative Optimization

scholarly journals Optimal Modelling of Steel Multi-Span Beams Using the Gradient-Iterative Method

2019 ◽  
Vol 29 (4) ◽  
pp. 112-127
Author(s):  
Leszek Mikulski
Keyword(s):  
Iterative Method ◽  
Optimal Solution ◽  
Optimization Method ◽  
Iterative Optimization ◽  
Basic Assumptions ◽  
The Web ◽  
Load Conditions

Abstract The article describes the gradient-iterative optimization method and outlines the method’s basic assumptions and illustrates its general use. The method’s implementation was illustrated based on a steel I-beam. The described calculation example concerns the optimization of the height of the web of a multi-span beam. The method enables finding an optimal solution with the use of simple and commonly available software. To illustrate the effectiveness of the optimization method, multiple calculations were performed for beams with various spans and various load conditions.

Non-linear Iterative Optimization Method for Locating Particles Using HPC Techniques

2014 ◽  
pp. 227-238
Author(s):  
Gloria Ortega ◽  
Julia Lobera ◽  
Inmaculada García ◽  
María del Pilar Arroyo ◽  
Gracia Ester Martín Garzón
Keyword(s):  
Optimization Method ◽  
Iterative Optimization ◽  
Non Linear
Sign in / Sign up

Export Citation Format

Share Document