This work develops a new robust statistical framework for blind image denoising. Robust statistics addresses the problem of estimation when the idealized assumptions about a system are occasionally violated. The contaminating noise in an image is considered as a violation of the assumption of spatial coherence of the image intensities and is treated as an outlier random variable. A denoised image is estimated by fitting a spatially coherent stationary image model to the available noisy data using a robust estimator-based regression method within an optimal-size adaptive window. The robust formulation aims at eliminating the noise outliers while preserving the edge structures in the restored image. Several examples demonstrating the effectiveness of this robust denoising technique are reported and a comparison with other standard denoising filters is presented.

Robust estimation approach for blind denoising

This paper presents a new methodology for the reduction of sensor noise from images acquired using digital cameras at high- International Organization for Standardization (ISO) and long- exposure settings. The problem lies in the fact that the algorithm must deal with hardware-related noise that affects certain color channels more than others and is thus nonuniform over all color channels. A new adaptive center-weighted hybrid mean and median filter is formulated and used within a novel optimal-size windowing framework to reduce the effects of two types of sensor noise, namely blue-channel noise and JPEG blocking artifacts, common in high-ISO digital camera images. A third type of digital camera noise that affects long-exposure images and causes a type of sensor noise commonly known as ''stuck-pixel'' noise is dealt with by pre- processing the image with a new stuck-pixel prefilter formulation. Experimental results are presented with an analysis of the perfor- mance of the various filters in comparison with other standard noise reduction filters. © 2004 SPIE and IS&T. (DOI: 10.1117/1.1668279)

Adaptive hybrid mean and median filtering of high-ISO long-exposure sensor noise for digital photography

Partial fringe orders can be extracted from photographic negatives of photoelastic fringes with reasonable accuracy. The accuracy can be improved by prefogging the film and adjusting the exposure so that saturation does not occur.

Application of the generalized maximum likelihood criterion to evaluation of partial fringe orders in photoelasticity

The paper describes a novel method for removing sensor noise from images acquired using digital cameras at high ISO settings. The problem lies in the fact that the algorithm must deal with hardware-related sensor noise that affects certain color channels more than others and is thus non-uniform over all color channels. An adaptive signal equalization mean and median hybrid filter is formulated and experimental results are presented with an analysis of the performance of the filter in comparison with other standard noise reduction filters.

Hybrid mean adaptive center weighted median filter for color sensor denoising

: One goal of this study has been to use the theoretical and experimental results on film characteristics obtained by photographic scientists in order to define film-grain noise within the context of estimation theory. A detailed model for the overall photographic imaging system is presented. There are linear blurring effects at the initial and the final segments of this model to account for various optical and chemical degradations. The middle segment of the model represents signal dependence effects of film-grain noise and includes a nonlinear noise term. The accuracy of this model is tested by simulating images according to it and comparing the results to images of similar targets that were actually recorded on film. These comparisons point out that the model is a good representation of the photographic imaging system. The restoration of images degraded by film-grain noise is considered in two different contexts - estimation theory and detection theory. Under the topic of estimation, a discrete Wiener filter is developed which explicitly allows for the signal- dependence of the noise. The filter adaptively alters its characteristics based on nonstationary first order statistics of an image. This filter is shown to have an advantage over the conventional Wiener filter.

Firouz Naderi

Papers

Estimation and Detection of Images Degraded by Film-Grain Noise