Secure single-sensor digital camera

2006 ◽  
Vol 42 (11) ◽  
pp. 627 ◽  
Author(s):  
R. Lukac ◽  
K.N. Plataniotis
Keyword(s):  
Digital Camera ◽  
Single Sensor

Iterative asymmetric average interpolation for color demosaicing of single-sensor digital camera data

2006 ◽  
Author(s):  
Yoshihisa Takahashi ◽  
Hisakazu Kikuchi ◽  
Shogo Muramatsu ◽  
Naoki Mizutani
Keyword(s):  
Digital Camera ◽  
Single Sensor

UDP – a New Universal Demosaicing Pipeline Algorithm for RGB Color Filter Arrays

2014 ◽  
Vol 11 (3) ◽  
pp. 16-29
Author(s):  
G. Zapryanov
Keyword(s):  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Subjective Evaluation ◽  
Digital Camera ◽  
Color Filter ◽  
Squared Error ◽  
Spectral Interpolation ◽  
Objective Metrics ◽  
Filter Array ◽  
Single Sensor

Abstract A new universal demosaicing method suitable for imaging pipelines using a RGB color filter array (CFA) is proposed. The proposed algorithm can reconstruct single-sensor images registered by a digital camera equipped with any RGB CFA currently in use. The algorithm includes a spatio-spectral interpolation model, a new edge-detection technique, and a post processing to reduce the color shifts and artifacts of the reconstructed image. Experimental results over various test CFA patterns and real images demonstrate the efficiency and feasibility of a universal demosaicing algorithm proposed in the paper. Two methods - objective metrics (mean squared error - MSE and peak signal to noise ratio - PSNR), and subjective evaluation are used to analyze the results

Using a Digital Camera for Documentation

2002 ◽  
Vol 12 (4) ◽  
pp. 145-146
Author(s):  
Steven C. Chang
Keyword(s):  
Digital Camera

A testing paradigm for quantifying ICC profilers

2019 ◽  
Vol 2019 (1) ◽  
pp. 80-85
Author(s):  
Pooshpanjan Roy Biswas ◽  
Alessandro Beltrami ◽  
Joan Saez Gomez
Keyword(s):  
Digital Camera ◽  
Visual Assessment ◽  
Just Noticeable Difference ◽  
Color Management ◽  
Color Gamut ◽  
Gamut Mapping ◽  
Digital File ◽  
Mapping Process ◽  
Testing Paradigm

To reproduce colors in one system which differs from another system in terms of the color gamut, it is necessary to use a color gamut mapping process. This color gamut mapping is a method to translate a specific color from a medium (screen, digital camera, scanner, digital file, etc) into another system having a difference in gamut volume. There are different rendering intent options defined by the International Color Consortium [5] to use the different reproduction goals of the user [19]. Any rendering intent used to reproduce colors, includes profile engine decisions to do it, i.e. looking for color accuracy, vivid colors or pleasing reproduction of images. Using the same decisions on different profile engines, the final visual output can look different (more than one Just Noticeable Difference[16]) depending on the profile engine used and the color algorithms that they implement. Profile performance substantially depends on the profiler engine used to create them. Different profilers provide the user with varying levels of liberty to design a profile for their color management needs and preference. The motivation of this study is to rank the performance of various market leading profiler engines on the basis of different metrics designed specifically to report the performance of particular aspects of these profiles. The study helped us take valuable decisions regarding profile performance without any visual assessment to decide on the best profiler engine.

A practical approach on non-regular sampling and universal demosaicing of raw image sensor data

2020 ◽  
Vol 2020 (1) ◽  
pp. 91-95
Author(s):  
Philipp Backes ◽  
Jan Fröhlich
Keyword(s):  
Image Quality ◽  
Source Code ◽  
Image Sensor ◽  
Sensor Data ◽  
Color Filter ◽  
Lowpass Filter ◽  
Sampling Errors ◽  
Regular Sampling ◽  
Single Sensor ◽  
Similar Image Quality

Non-regular sampling is a well-known method to avoid aliasing in digital images. However, the vast majority of single sensor cameras use regular organized color filter arrays (CFAs), that require an optical-lowpass filter (OLPF) and sophisticated demosaicing algorithms to suppress sampling errors. In this paper a variety of non-regular sampling patterns are evaluated, and a new universal demosaicing algorithm based on the frequency selective reconstruction is presented. By simulating such sensors it is shown that images acquired with non-regular CFAs and no OLPF can lead to a similar image quality compared to their filtered and regular sampled counterparts. The MATLAB source code and results are available at: http://github. com/PhilippBackes/dFSR

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