Dark-frame subtraction

About: Dark-frame subtraction is a research topic. Over the lifetime, 1216 publications have been published within this topic receiving 20763 citations.

Image quality evaluation based on image weighted separating block peak signal to noise ratio

Abstract: Traditional objective image quality evaluation measures, such as the MSE or the PSNR, only represent the total difference between the original images and reconstructed images. However in some case, such as there are few large error pixels and many small error pixels in an image, they have not a consistent a result with subjective measure. To cope with this drawback, we propose a new image quality evaluation measure that is called geometry weighted separating block peak signal to noise ratio (GWSB/spl I.bar/PSNR). It corresponds to human visual observations very well. The experimental result shows that this measure is valid, reliable, wieldy and extensible.

Tracking apparatus and methods using image processing noise reduction

Abstract: Improved apparatus and methodology for image processing and object tracking that, inter alia, reduces noise. In one embodiment, the methodology is applied to moving targets such as missiles in flight, and comprises processing sequences of images that have been corrupted by one or more noise sources (e.g., sensor noise, medium noise, and/or target reflection noise). In this embodiment, a multi-dimensional image is acquired for a first time step t; the acquired image is normalized and sampled, and then segmented into target and background pixel sets. Intensity statistics of the pixel sets are determined, and a prior probability image from a previous time step smoothed. The smoothed prior image is then shifted to produce an updated prior image, and a posterior probability image calculated using the updated prior probability. Finally, the position of the target is extracted using the posterior probability image. A tracking system and controller utilizing this methodology are also disclosed.

A two-sensor noise reduction system: applications for hands-free car kit

Abstract: This paper presents a two-microphone speech enhancer designed to remove noise in hands-free car kits. The algorithm, based on the magnitude squared coherence, uses speech correlation and noise decorrelation to separate speech from noise. The remaining correlated noise is reduced using cross-spectral subtraction. Particular attention is focused on the estimation of the different spectral densities (noise and noisy signals power spectral densities) which are critical for the quality of the algorithm. We also propose a continuous noise estimation, avoiding the need of vocal activity detector. Results on recorded signals are provided, showing the superiority of the two-sensor approach to single microphone techniques.

Characterization of noise in digital photographs for image processing

Abstract: Many conventional image processing algorithms such as noise filtering, sharpening and deblurring, assume a noise model of Additive White Gaussian Noise (AWGN) with constant standard deviation throughout the image. However, this noise model does not hold for images captured from typical imaging devices such as digital cameras, scanners and camera-phones. The raw data from the image sensor goes through several image processing steps such as demosaicing, color correction, gamma correction and JPEG compression, and thus, the noise characteristics in the final JPEG image deviates significantly from the widely-used AWGN noise model. Thus, when the image processing algorithms are applied to the digital photographs, they may not provide optimal image quality after the image processing due to the inaccurate noise model. In this paper, we propose a noise model that better fits the images captured from typical imaging devices and describe a simple method to extract necessary parameters directly from the images without any prior knowledge of imaging pipeline algorithms implemented in the imaging devices. We show experimental results of the noise parameters extracted from the raw and processed digital images.