Analysis of focus measure operators for shape-from-focus

This paper presents a passive auto-focus camera control system which can easily achieve the function of auto-focus with no necessary of any active component (e.g., infrared or ultrasonic sensor) in comparison with the conventional active focus system. To implement the technique we developed, the hardware system including the adjustable lens with CMOS sensor and servo motor, an 8051 image capture micro-controller, a field programmable gate array (FPGA) sharpness measurement circuit, a pulse width modulation (PWM) controller, and a personal digital assistant (PDA) image displayer was constructed. The discrete wavelet transformation (DWT), the morphology edge enhancement sharpness measurement algorithms, and the self-organizing map (SOM) neural network were used in developing the control mechanism of the system. Compared with other passive auto-focus methods, the method we proposed has the advantages of lower computational complexity and easier hardware implementation.

A passive auto-focus camera control system

Gaussian Pre-Filtering for Uncertainty Minimization in Digital Image Correlation Using Numerically-Designed Speckle Patterns

An essential and indispensable component of automated microscopy framework is the automatic focusing system, which determines the in-focus position of a given field of view by searching the maximum value of a focusing function over a range of z-axis positions. The focus function and its computation time are crucial to the accuracy and efficiency of the system. Sixteen focusing algorithms were analyzed for histological and histopathological images. In terms of accuracy, results have shown an overall high performance by most of the methods. However, we included in the evaluation study other criteria such as computational cost and focusing curve shape which are crucial for real-time applications and were used to highlight the best practices.

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Autofocus evaluation for brightfield microscopy pathology

We review camera architecture in the age of artificial intelligence. Modern cameras use physical components and software to capture, compress and display image data. Over the past 5 years, deep learning solutions have become superior to traditional algorithms for each of these functions. Deep learning enables 10-100x reduction in electrical sensor power per pixel, 10x improvement in depth of field and dynamic range and 10-100x improvement in image pixel count. Deep learning enables multiframe and multiaperture solutions that fundamentally shift the goals of physical camera design. Here we review the state of the art of deep learning in camera operations and consider the impact of AI on the physical design of cameras.

Smart Cameras

A new passive autofocus algorithm consisting of a robust focus measure for object detection and fuzzy reasoning for target selection is presented. The proposed algorithm first detects objects distributed in the image using a mid-frequency discrete cosine transform focus measure and then selects the target object through fuzzy reasoning with three fuzzy membership functions. The proposed algorithm is designed as full digital blocks and fabricated using 0.35-mum CMOS technology. Experimental results obtained from various samples show that the present method is insensitive to Gaussian and impulsive noises and able to improve the quality of the image by focusing to the appropriate target object.

Ji-Man Cho

Papers

Enhanced Autofocus Algorithm Using Robust Focus Measure and Fuzzy Reasoning