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 sensor with dark signal reduction

Abstract: An image sensor that has a temperature sensor. The temperature sensor senses the temperature of a pixel array of the image sensor. The sensed temperature is used to scale a dark frame image generated by the pixel array. The scaled dark frame image is subtracted from a light image frame generated by the pixel array. The scaled dark image frame compensates for temperature variations in the pixel array. The scaled dark image frame may be generated by multiplying the dark frame by a scale factor(s). The scale factor may be computed from an equation or determined from a look-up table. The equation or look-up table may compensate for thermal gradients across the pixel.

A new edge-detection based noise removal algorithm

Abstract: A method is disclosed for removing noise by distinguishing between edge and non-edge pixels, (120, 130, 140) and applying a first noise removal technique to pixels classified as non-edge pixels, (160) and a second noise removal technique for pixels classified as edge pixels, (150). The methodology operates on images while in a Color Filter Array (CFA) domain, prior to color interpolation and uses techniques suited to the classification, whether edge or non-edge.

Applying perceptually correct 3D film noise

Abstract: Perceptually correct noises simulating a variety of noise patterns or textures may be applied to stereo image pairs each of which comprises a left eye (LE) image and a right eye (RE) image that represent a 3D image. LE and RE images may or may not be noise removed. Depth information of pixels in the LE and RE images may be computed from, or received with, the LE and RE images. Desired noise patterns are modulated onto the 3D image or scene so that the desired noise patterns are perceived to be part of 3D objects or image details, taking into account where the 3D objects or image details are on a z-axis perpendicular to an image rendering screen on which the LE and RE images are rendered.

Locally adaptive nonlinear noise reduction

Abstract: An imaging scanner (10) acquires imaging data. A reconstruction processor (30) reconstructs the imaging data into an unfiltered reconstructed image. A local noise mapping processor (64, 120, 136, 140, 142, 152) generates a noise map (68, 68', 68'') representative of spatially varying noise characteristics in the unfiltered reconstructed image. A locally adaptive non linear noise filter (60) differently filters different regions of the unfiltered reconstructed image in accordance with the noise map (68, 68', 68'') to produce a filtered reconstructed image.

An improved camera identification method based on the texture complexity and the image restoration

Abstract: The identification of source camera is useful to improve the capability of evidence in the digital image such as distinguish the photographer taking illegal images and adopting digital images as evidence of crime. Lukas, et al. showed the method for source camera identification based on the correlation of PNU (pixel nonuniformity) noise. However, the wavelet-based denoising filter for suppressing the random noise reduces the accuracy of camera identification. It is caused by the fact that the denoising filter diffuses the edge and makes the PNU noise less pronounced. Moreover, it is difficult to extract PNU noise from the images taken by cameras which are equipped with the image improvement functions such as motion blur correction, contrast enhancement, and noise reduction. In this paper, we propose a method for improving the camera identification accuracy by selecting pixels based on the texture complexity. We also propose a-method for improving the identification accuracy by applying the image restoration method.