Recently, many efficient methods have been developed for dehazing using a single observed image. Such dehazing algorithms estimate scene depths and then compute the thickness of haze. However, since the problem is ill-posed, the restored image often contains artificial colors and overstretched contrast. In this work, we use multiple capturing of hazed images with three cameras to solve the dehazing problem. A new dehazing method with three multiple images is based on solution of explicit linear systems of equations derived from optimization of an objective function. The performance of the proposed method is compared with that of common dehazing algorithm in terms of accuracy of the quality of image restoration.
In this work, we propose a method to restore color images from a set of degraded color images obtained with a microscanning imaging system. Using the set of observed images, image restoration is carried out by solving a system of equations that is derived from optimization of an objective function. Since the proposed method possesses a high computational complexity, a fast algorithm is developed. Experimental and computer simulation results obtained with the proposed method are analyzed in terms of restoration accuracy and tolerance to additive input noise.
Various techniques for image recovery from degraded observed images using microscanning image system were
proposed. The methods deal with additive, multiplicative interferences, and sensor's noise. Basically, they use several
observed images captured with small spatial shifts. In this paper, we analyze the tolerance of restoration methods to shift
errors during camera microscanning. Computer simulation results obtained with the restoration methods using degraded
images from an imperfect microscanning system are compared with those of ideal microscanning system in terms of
Various techniques for image recovery from degraded observed images were proposed. Most of the methods deal with
linear degradations and carry out signal processing using a single observed image. In this paper multiplicative, additive,
and impulsive image degradations are investigated. We propose restoration algorithms based on three observed degraded
images obtained from a microscanning camera. It is assumed that degraded images contain information about an original
image, illumination function, and noise. Using three degraded images and mathematical model of degradation a set of
equations is formed. By solving the system of equations with the help of an iterative algorithm the original image is
Recently, a blind image restoration algorithm based on camera microscanning was proposed. Unfortunately, the
computational complexity of the algorithm is very high. In this paper we propose a fast algorithm for image
restoration using the information obtained during camera microscanning. First, the captured observed images are
decomposed into a pyramidal set of small images. Next, the blind iterative algorithm is applied for restoration of the
set of small images. Finally, the resultant output image is constructed from the set of restored small images.
Simulation results are presented and discussed. Preliminary results show that the processing time is significantly
Sharpened cantilevered nozzles were fabricated combining microsystem technologies and focused ion beam
micromachining. Micronozzles consist of silicon chips with silicon oxide microchannels whose micronozzles were
reshaped using Focused Ion Beam. Micronozzle body was defined by an aluminum sacrificial layer patterned over a
silicon wafer. This layer was surrounded by a deposited silicon oxide structural layer. The chip is defined by a silicon
deep reactive ion etching through the wafer. This process releases part of the metal line forming a cantilevered
micronozzle. Sharp reshaped micronozzles were achieved by focused ion beam milling. Mechanical tests of silicon oxide nozzles still containing the aluminum sacrificial layer were performed by cell piercing. In some instances, zona pellucida and membrane were crossed without cell lysis, and micronozzles remained intact.
Common restoration techniques perform signal processing using a single observed image. In this paper we show that the accuracy of restoration could be significantly increased if at least three observed degraded images obtained from a microscanning camera are used. It is assumed that the degraded images contain information about an original image, linear degradation and illumination functions, and additive sensor's noise. Using spatial information from camera, a set of equations and objective function are formed. By solving the system of equations with the help of an iterative algorithm, the original image can be recovered. Computer simulation results presented and discussed.