This paper presents the design of an accurate rain model for the commercially-available Anyverse automotive simulation environment. The model incorporates the physical properties of rain and a process to validate the model against real rain is proposed. Due to the high computational complexity of path tracing through a particle-based model, a second more computationally efficient model is also proposed. For the second model, the rain is modeled using a combination of a particle-based model and an attenuation field. The attenuation field is fine-tuned against the particle-only model to minimize the difference between the models.
Optimizing exposure time for low light scenarios involves a trade-off between motion blur and signal to noise ratio. A method for defining the optimum exposure time for a given function has not been described in the literature. This paper presents the design of a simulation of motion blur and exposure time from the perspective of a real-world camera. The model incorporates characteristics of real-world cameras including the light level (quanta), shot noise and lens distortion. In our simulation, an image quality target chart called the Siemens Star chart will be used, and the simulation outputs a blurred image as if captured from a camera of set exposure and set movement speed. The resulting image is then processed in Imatest in which image quality readings will be extracted from the image and consequently the relationship between exposure time, motion blur and the image quality metrics can be evaluated.
The Noise Power Spectrum (NPS) is a standard measure for image capture system noise. It is derived traditionally from captured uniform luminance patches that are unrepresentative of pictorial scene signals. Many contemporary capture systems apply nonlinear content-aware signal processing, which renders their noise scene-dependent. For scene-dependent systems, measuring the NPS with respect to uniform patch signals fails to characterize with accuracy: i) system noise concerning a given input scene, ii) the average system noise power in real-world applications. The sceneand- process-dependent NPS (SPD-NPS) framework addresses these limitations by measuring temporally varying system noise with respect to any given input signal. In this paper, we examine the scene-dependency of simulated camera pipelines in-depth by deriving SPD-NPSs from fifty test scenes. The pipelines apply either linear or non-linear denoising and sharpening, tuned to optimize output image quality at various opacity levels and exposures. Further, we present the integrated area under the mean of SPD-NPS curves over a representative scene set as an objective system noise metric, and their relative standard deviation area (RSDA) as a metric for system noise scene-dependency. We close by discussing how these metrics can also be computed using scene-and-processdependent Modulation Transfer Functions (SPD-MTF).
This paper describes a CMOS image sensor (CIS) horizontal band noise reduction methodology considering on-chip and offchip camera module PCB design parameters. The horizontal band noise is a crucial issue for high quality camera of modern smartphone applications. This paper discusses CIS horizontal band noise mechanism and proposes the solution by optimization of design factors in CIS and camera module. Analog ground impedance value and bias voltage condition of pixel array transfer gate have been found to be effective optimization parameters. Through the real experimental data, we proved that proposed solution is instrumental in reducing the horizontal band noise.
In order to explore the design space of a new, potentially unconventional, sensor or to optimize sensor characteristics for a given computer vision application, an image acquisition process simulator has been designed. Its aim is to be simple and modular, yet complete and accurate enough to match the physical phenomena involved. The approach has been described in this paper to highlight the different steps of the acquisition process and to explain the implementation choices and the hypotheses that were made. The simulator has been tested on images of point sources, on simulated test patterns and on real high definition pictures and has proven realistic.