Advanced statistical tools for enhanced quality digital imaging with realistic capture models

Aleksandra Pižurica,Keigo Hirakawa,Javier Portilla,Karen Egiazarian

doi:10.1186/1687-6180-2013-140

Abstract

Editorial Getting closer to reality in modeling image capture devices is crucial for the improvement of image quality beyond the limits of image restoration algorithms as we know them today. This calls for more accurate statistical modeling of distortions and noise coming from real capture devices (Poisson noise, internal non-linearities, space variant point spread functions due to nonideal optics, chromatic aberrations, etc.). While these effects are often not considered in the restoration algorithms, their impact on the resulting image quality is huge in practice. For example, different nonlinearities (both intrinsic to the imaging device and induced ones, e.g., to make the noise signal independent) can invalidate typically assumed noise models and can also devastate deblurring. Joint modeling of digital and nondigital components (like optics and sensors) or various sources of image distortions (such as color filter array, blur, and noise) will likely yield improvements over the traditional approach to treat them separately. Rapid progress in digital camera technology makes a huge impact on computer vision, surveillance and security systems, production of portable electronic devices, such as smart phones. Physical limits of the sensors are likely to impose trade-offs on picture quality (e.g., in terms of achievable resolution versus noise considerations) that can be dealt with only by smart and device-aware signal processing. Moreover, new challenges arise from new imaging technologies, like in three-dimensional (3D) digital cameras [1,2], and new acquisition modalities, such as compressive sensing [3-7]. One of the central topics to this special issue is realistic modeling of noise in digital cameras.With ongoingminiaturization, sensor elements of the camera are becoming increasingly sensitive to noise. In state-of-the-art image sensors, the pixel size is approaching 1 μm. By shrinking the pixel size towards the wavelengths of light that

Highlights

Editorial Getting closer to reality in modeling image capture devices is crucial for the improvement of image quality beyond the limits of image restoration algorithms as we know them today
Due to the mismatch between the actual noise characteristics and typically assumed, simplified noise models, the noise level is typically over- or underestimated, affecting adversely the performance of noise reduction. Three papers in this special issue [9,10,11] address the problem of camera noise modeling and different aspects of noise reduction, ranging from post-processing steps for hardware solutions to purely signal processing postproduction approaches
Binning in color images is challenging due to the presence of color filter array (CFA)

Summary

Introduction

Editorial Getting closer to reality in modeling image capture devices is crucial for the improvement of image quality beyond the limits of image restoration algorithms as we know them today. Recent improvements in sensor sensitivity allow cameras to operate in very low lighting conditions, but this boosts noise in the acquired images. Three papers in this special issue [9,10,11] address the problem of camera noise modeling and different aspects of noise reduction, ranging from post-processing steps for hardware solutions to purely signal processing postproduction approaches.

Results

Conclusion