Spatio-temporal Noise Reduction Research Articles

Improved Spatiotemporal Noise Reduction for Very Low-Light Environments

In this brief, an improved spatiotemporal noise reduction algorithm for images that are acquired through a complementary metal-oxide semiconductor image sensor (CIS) in very low-light environments is presented. To reduce the strong noises amplified by an automatic gain controller in the dark, a motion detection process is used to determine how the pixels from a spatial noise filter and a temporal noise filter are adaptively blended. The proposed algorithm also amends fixed-pattern noise, which is caused by the allowable pixel deviation of the CIS before the image is fed to the noise reduction filters. After the low-light noise is reduced, the color distortion in the low-light environments is fixed in the hue, saturation, value color space. The proposed algorithm ultimately achieved more favorable objective and subjective image qualities than the existing algorithms, as shown in the experimental results.

Movement Awareness-Adaptive Spatio Temporal Noise Reduction in Video

In this letter, we present an efficient video matching-based denoising method. Two main issues are addressed in this paper: the matched points and the denoising algorithm based on an adaptive spatial temporal filter. Unlike previous algorithms, our method adaptively selects reference pixels within spatially and temporally neighboring frames. Our method uses more information about matched pixels on neighboring frames than other methods. Therefore, the proposal enhanced the accuracy of video denoising. Simulation results show that the proposed method produces cleaner and sharper images.

Quantitative image quality analysis of a nonlinear spatio-temporal filter

Digital temporal and spatial filtering of fluoroscopic image sequences can be used to improve the quality of images acquired at low X-ray exposure. In this study, we characterized a nonlinear edge preserving, spatio-temporal noise reduction filter, the bidirectional multistage (BMS) median filter of Arce (1991). To assess image quality, signal detection and discrimination experiments were performed on stationary targets using a four-alternative forced-choice paradigm. A measure of detectability, d', was obtained for filtered and unfiltered noisy image sequences at different signal amplitudes. Filtering gave statistically significant, average d' improvements of 20% (detection) and 31% (discrimination). A nonprewhitening detection model modified to include the human spatio-temporal visual system contrast-sensitivity underestimated enhancement, predicting an improvement of 6%. Pixel noise standard deviation, a commonly applied image quality measure, greatly overestimated effectiveness giving 67% improvement in d'. We conclude that human testing is required to evaluate the filter effectiveness and that human perception models must be improved to account for the spatio-temporal filtering of image sequences.

Perception adaptive temporal TV-noise reduction using contour preserving prefilter techniques

The reduction of Gaussian noise is still an important task in video systems. For this purpose a spatio-temporal noise reduction scheme for interlaced video is presented. It consists mainly of a subband based temporal recursive filter which makes use of some special properties of the human visual system. This temporal system is supported by a preceding detail preserving spatial filter with low hardware expense, which consists of an image analysing highpass filter bank and an adaptive lowpass FIR-filter for noise reduction. Both the spatial and temporal noise reduction have been evaluated with a large amount of simulations which result in a very good objective and subjective efficiency. Furthermore the chain of both temporal and spatial noise reduction may even yield results which are better than the sum of pure spatial and temporal techniques.