A derivative-based fast autofocus method

Most automatic focusing methods are based on a sharpness function, which delivers a real-valued estimate of an image quality. In this paper, we study an L2-norm derivative-based sharpness function, which has been used before based on heuristic consideration. We give a more solid mathematical foundation for this function and get a better insight into its analytical properties. Moreover an efficient autofocus method is presented, in which an artificila blur variable plays an important role. We show that for a specific choice of the artificial blur control variable, the function is approximately a quadratic polynomial, which implies that after obtaining of at least three images one can find the approximate position of the optimal defocus. This provides the speed improvement in comparison with existing approaches, which usually require recording of more than ten images for autofocussing. The new autofocus method is employed for the scanning transmission electron microscopy. To be more specific, it has been implemented in the FEI scanning transmission electron microscope and its performance has been tested as a part of a particle analysis application

Modeling and applications of the focus cue in conventional digital cameras

El enfoque en cámaras digitales juega un papel fundamental tanto en la calidad de la imagen como en la percepción del entorno. Esta tesis estudia el enfoque en cámaras digitales convencionales, tales como cámaras de móviles, fotográficas, webcams y similares. Una revisión rigurosa de los conceptos teóricos detras del enfoque en cámaras convencionales muestra que, a pasar de su utilidad, el modelo clásico del thin lens presenta muchas limitaciones para aplicación en diferentes problemas relacionados con el foco. En esta tesis, el focus profile es propuesto como una alternativa a conceptos clásicos como la profundidad de campo. Los nuevos conceptos introducidos en esta tesis son aplicados a diferentes problemas relacionados con el foco, tales como la adquisición eficiente de imágenes, estimación de profundidad, integración de elementos perceptuales y fusión de imágenes. Los resultados experimentales muestran la aplicación exitosa de los modelos propuestos.The focus of digital cameras plays a fundamental role in both the quality of the acquired images and the perception of the imaged scene. This thesis studies the focus cue in conventional cameras with focus control, such as cellphone cameras, photography cameras, webcams and the like. A deep review of the theoretical concepts behind focus in conventional cameras reveals that, despite its usefulness, the widely known thin lens model has several limitations for solving different focus-related problems in computer vision. In order to overcome these limitations, the focus profile model is introduced as an alternative to classic concepts, such as the near and far limits of the depth-of-field. The new concepts introduced in this dissertation are exploited for solving diverse focus-related problems, such as efficient image capture, depth estimation, visual cue integration and image fusion. The results obtained through an exhaustive experimental validation demonstrate the applicability of the proposed models

Autofocus for digital Fresnel holograms by use of a Fresnelet-sparsity criterion

We propose a robust autofocus method for reconstructing digital Fresnel holograms. The numerical reconstruction involves simulating the propagation of a complex wave front to the appropriate distance. Since the latter value is difficult to determine manually, it is desirable to rely on an automatic procedure for finding the optimal distance to achieve high-quality reconstructions. Our algorithm maximizes a sharpness metric related to the sparsity of the signal’s expansion in distance-dependent waveletlike Fresnelet bases. We show results from simulations and experimental situations that confirm its applicability

All-optical microscope autofocus based on an electrically tunable lens and a totally internally reflected IR laser

Funding Information: Deutsche Forschungsgemeinschaft (SFB 688); NIH (Prime Grant No. 5 R01 DA038882-02); University of Wuerzburg.Microscopic imaging at high spatial-temporal resolution over long time scales (minutes to hours) requires rapid and precise stabilization of the microscope focus. Conventional and commercial autofocus systems are largely based on piezoelectric stages or mechanical objective actuators. Objective to sample distance is either measured by image analysis approaches or by hardware modules measuring the intensity of reflected infrared light. We propose here a truly all-optical microscope autofocus taking advantage of an electrically tunable lens and a totally internally reflected infrared probe beam. We implement a feedback-loop based on the lateral position of a totally internally reflected infrared laser on a quadrant photodetector, as an indicator of the relative defocus. We show here how to treat the combined contributions due to mechanical defocus and deformation of the tunable lens. As a result, the sample can be kept in focus without any mechanical movement, at rates up to hundreds of Hertz. The device requires only reflective optics and can be implemented at a fraction of the cost required for a comparable piezo-based actuator.Publisher PDFPeer reviewe

Generation of All-in-Focus Images by Noise-Robust Selective Fusion of Limited Depth-of-Field Images

The limited depth-of-field of some cameras prevents them from capturing perfectly focused images when the imaged scene covers a large distance range. In order to compensate for this problem, image fusion has been exploited for combining images captured with different camera settings, thus yielding a higher quality all-in-focus image. Since most current approaches for image fusion rely on maximizing the spatial frequency of the composed image, the fusion process is sensitive to noise. In this paper, a new algorithm for computing the all-in-focus image from a sequence of images captured with a low depth-of-field camera is presented. The proposed approach adaptively fuses the different frames of the focus sequence in order to reduce noise while preserving image features. The algorithm consists of three stages: 1) focus measure; 2) selectivity measure; 3) and image fusion. An extensive set of experimental tests has been carried out in order to compare the proposed algorithm with state-of-the-art all-in-focus methods using both synthetic and real sequences. The obtained results show the advantages of the proposed scheme even for high levels of noise