CCD Noise Effect on Data Transmission Efficiency of Onboard Lossless-Compressed Remote Sensing Images

Wu, Qiang; Chi, Yaobin; Wang, Zhiyong

doi:10.1109/iciecs.2009.5365161

Cited by 1 publication

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since noise sources are random in nature, their values must be handled as random variables, described by probabilistic functions [9]. In fact, Dark Current, proportional to the integration time and temperature, is modelled as a Gaussian distribution, Shot and Read Noise, caused by on-chip output amplifiers, are modelled as Poisson distributions, and, detector malfunction or hot pixels are modeled by an impulsive distribution [10]. In most cases, all Gaussian and Poisson distributed noises are combined, approximating the image noise with an equivalent additive zero-mean white Gaussian noise distribution, characterized by a variance 2 n [2].…”

Section: Introductionmentioning

confidence: 99%

AIDI: An adaptive image denoising FPGA-based IP-core for real-time applications

Carlo

Prinetto

Rolfo

et al. 2013

2013 NASA/ESA Conference on Adaptive Hardware and Systems (AHS-2013)

View full text Add to dashboard Cite

The presence of noise in images can significantly impact the performances of digital image processing and computer vision algorithms. Thus, it should be removed to improve the robustness of the entire processing flow. The noise estimation in an image is also a key factor, since, to be more effective, algorithms and denoising filters should be tuned to the actual level of noise. Moreover, the complexity of these algorithms brings a new challenge in real-time image processing applications, requiring high computing capacity. In this context, hardware acceleration is crucial, and Field Programmable Gate Arrays (FPGAs) best fit the growing demand of computational capabilities. This paper presents an Adaptive Image Denoising IP-core (AIDI) for realtime applications. The core first estimates the level of noise in the input image, then applies an adaptive Gaussian smoothing filter to remove the estimated noise. The filtering parameters are computed on-the-fly, adapting them to the level of noise in the image, and pixel by pixel, to preserve image information (e.g., edges or corners). The FPGA-based architecture is presented, highlighting its improvements w.r.t. a standard static filtering approach.

show abstract

Section: Introductionmentioning

confidence: 99%