Inverse filtering of optical images

Abstract

The quality of images is limited by the performance of the optical system used. The imperfections of the optical system cause distortion of the image. If the distortion is known it can be (partly) compensated. This procedure is called inverse filtering. The problem is, however, ill-posed, which means that the measurement noise is amplified by the inverse filtering process. Suppression of the noise causes bias in the reconstruction. A tradeoff has to be found between the noisy and biased estimates. In this paper, the reconstruction of images will be investigated, assuming that the distortion of the optical system is known. An algorithm will be introduced to estimate the optimal level of noise suppression of the two-dimensional inverse filter.

Restoration of Blurred and Noisy Images Using Inverse Filtering and Adaptive Threshold Method

“…The image degradations may include blurring due to camera motion, for example, [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] or noise which is effectively equivalent to errors in the image pixel values and is due to many causes such as electronic image transmission [19][20][21][22][23][24][25][26][27]. Law enforcement, for example, is an application of image restoration in which the image is blurred due to motion.…”

“…In [6], a blind deconvolution method is proposed to restore ultrasound images based on inverse filtering with a restoration kernel. A method is proposed in [7] to estimate the optimum level of noise elimination of the two-dimensional inverse filter. A comparison between inverse filter and Wiener filter in reducing noise and blur is shown in [8].…”

Deconvolution processing for increasing the resolution of magnetic force microscopy measurements

“…Indeed, deconvolution processing has been used in addressing problems in fields as diverse as astronomy [11,12], remote sensing [13,14], medicine [15] and multimedia [16]. Regardless of the domain of application, image deconvolution is concerned with recovering an image (in the presence of noise) given the known spatially-invariant response of the sensor that degraded the original image [17,18] and these problems have been approached using methods based on inverse filtering [19], direct regularised restoration (e.g. the Wiener filter) [20], iterative inversion [21] and via recursive means utilising Kalman filter variants To appear in Journal of Nanotechnology: Nanoscale Devices and Systems Integration Special Issue 4 [22], among others.…”