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 processing apparatus, image processing method, and computer readable storage medium which stores image processing program

Abstract: An image processing apparatus includes a noise reduction unit which performs noise reduction processing to image signals, a first noise presumption unit which presumes a first noise amount from a present image signal among the image signals, and a second noise presumption unit which presumes a second noise amount based on the first noise amount, the present image signal, and the image signal of the past which underwent the noise reduction processing. The noise reduction unit performs the noise reduction processing to the present image signal based on the second noise amount.

A mechanical-noise suppressor based on a priori noise information for digital still cameras and camcorders

Abstract: This paper proposes a mechanical-noise suppressor based on a priori noise information for digital still cameras and camcorders. With given timing information about noise periods, mechanical noise generated by motors and lens-actuators is suppressed based on reference noise data prepared in advance. The noise replica is generated from the reference noise and subtracted from the input signal for suppression. Individual and aging differences in the reference noise are automatically adjusted by scaling the reference such that the residual noise is minimized. Simulation results demonstrate that the mechanical noise generated by zooming and auto-focusing are successfully suppressed.

A decision based switching median filter for restoration of images corrupted by high density impulse noise

Abstract: Digital images are corrupted by impulse noise mainly due to sensor faults of image acquisition devices and adverse channel environment which in turn degrades the image quality. A decision based switching median filter (DBSMF) to restore images corrupted with high density impulse noise is proposed in this paper. The global use of standard median filters for impulse noise removal from corrupted images provide good results but the filtering operation may affect fine pixels in addition to noisy pixels which leaves a blurred effect on the filtered image. In order to address this issue the proposed algorithm makes use of an efficient detection scheme to identify the noise pixels and noise free pixels. The detection algorithm clusters the pixels in the corrupted image so as to fall under three categories which states whether the pixels are corrupted or uncorrupted. The proposed switching median filter processes only on those pixels that are classified as corrupted and replaces the processing pixel by the median value. Under high noise densities the filtering window consists of more number of corrupted pixels. For such cases, the proposed algorithm restricts certain conditions on the expansion of the filtering window size to effectively choose the median value. The performance of this decision based algorithm is tested against four noise models for different levels of noise densities and is evaluated in terms of performance metrics which include Peak Signal to Noise ratio (PSNR) and Image Enhancement Factor (IEF). It gives better results for images that are extremely corrupted up to 90% noise density and outperforms classic filters in terms of handling image corruption.

Trimmed Non-local Means Technique for Mixed Noise Removal in Color Images

Abstract: In this paper a novel approach to the mixed noise removal in color images is proposed. The described method is a generalization of the Non-Local Means algorithm, where the pixels in the filtering window are ordered and only the most centrally located pixels in the filtering window are considered and used to calculate the weights needed for the averaging operation. The comparison with the existing state-of-the-art denoising schemes in terms of image restoration quality measures shows, that the new approach yields significantly better results in suppressing mixed noise in color digital images.

Noise estimation for remote sensing image data analysis

Abstract: Noise estimation does not receive much attention in remote sensing society. It may be because normally noise is not large enough to impair image analysis result. Noise estimation is also very challenging due to the randomness nature of the noise (for random noise) and the difficulty of separating the noise component from the signal in each specific location. We review and propose seven different types of methods to estimate noise variance and noise covariance matrix in a remotely sensed image. In the experiment, it is demonstrated that a good noise estimate can improve the performance of an algorithm via noise whitening if this algorithm assumes white noise.