Query: autofocus

title: Deep learning for single shot autofocus microscopy
abstract: Maintaining an in focus image over long time scales is an essential and nontrivial task for a variety of microscopy applications. Here, we describe a fast, robust autofocusing method compatible with a wide range of existing microscopes. It requires only the addition of one or a few off axis illumination sources (e.g. LEDs) and can predict the focus correction from a single image with this illumination. We designed a neural network architecture, the fully connected Fourier neural network (FCFNN) that exploits an understanding of the physics of the illumination to make accurate predictions with 2 3 orders of magnitude fewer learned parameters and less memory usage than existing state of the art architectures, allowing it to be trained without any specialized hardware. We provide an open source implementation of our method, to enable fast, inexpensive autofocus compatible with a variety of microscopes.

title: AutoFocus: Efficient Multi Scale Inference
abstract: This paper describes AutoFocus, an efficient multi scale inference algorithm for deep learning based object detectors. Instead of processing an entire image pyramid, AutoFocus adopts a coarse to fine approach and only processes regions which are likely to contain small objects at finer scales. This is achieved by predicting category agnostic segmentation maps for small objects at coarser scales, called FocusPixels. FocusPixels can be predicted with high recall, and in many cases, they only cover a small fraction of the entire image. To make efficient use of FocusPixels, an algorithm is proposed which generates compact rectangular FocusChips which enclose FocusPixels. The detector is only applied inside FocusChips, which reduces computation while processing finer scales. Different types of error can arise when detections from FocusChips of multiple scales are combined, hence techniques to correct them are proposed. AutoFocus obtains an mAP of 47.9% (68.3% at 50% overlap) on the COCO test dev set while processing 6.4 images per second on a Titan X (Pascal) GPU. This is 2.5X faster than our multi scale baseline detector and matches its mAP. The number of pixels processed in the pyramid can be reduced by 5X with a 1% drop in mAP. AutoFocus obtains more than 10% mAP gain compared to RetinaNet but runs at the same speed with the same ResNet 101 backbone.

title: An Effective Autofocus Method for Fast Factorized Back Projection
abstract: Back projection (BP) is a reliable synthetic aperture radar (SAR) imaging algorithm because of its high resolution and strong adaptability. However, it is hard to implement because of its high computational complexity. Fast factorized BP (FFBP) is a new way to fix this problem. Like traditional BP, FFBP is compatible with arbitrary flight paths if the track deviations are measured within fractions of a wavelength. However, when the motion information is not accurate enough, autofocus become an important way to get well focused images. In this paper, we present an effective autofocus method for FFBP to solve the imaging problem caused by platform's motion errors. First, an image quality evaluation function with unknown phase error based on image sharpness for FFBP is established. Then, the phase error computation for autofocus is modeled as an optimization problem. Second, the coordinate descent (CD) and secant processing are introduced to the maximum image sharpness problem. The proposed method keeps the rapid imaging performance of FFBP and solves well the motion error compensation problem. In the end, simulated data and real data were used to verify the effectiveness of the proposed algorithm.

title: Autofocus Layer for Semantic Segmentation
abstract: We propose the autofocus convolutional layer for semantic segmentation with the objective of enhancing the capabilities of neural networks for multi scale processing. Autofocus layers adaptively change the size of the effective receptive field based on the processed context to generate more powerful features. This is achieved by parallelising multiple convolutional layers with different dilation rates, combined by an attention mechanism that learns to focus on the optimal scales driven by context. By sharing the weights of the parallel convolutions we make the network scale invariant, with only a modest increase in the number of parameters. The proposed autofocus layer can be easily integrated into existing networks to improve a model's representational power. We evaluate our models on the challenging tasks of multi organ segmentation in pelvic CT and brain tumor segmentation in MRI and achieve very promising performance.

title: A Fast Eigenvector Based Autofocus Method for Sparse Aperture ISAR Sensors Imaging of Moving Target
abstract: Motion compensation is of significant importance in generating high focused inverse synthetic aperture radar (ISAR) images. The range misalignment and the phase error of each received echo are two important parameters in ISAR imaging and they need to be precisely estimated and compensated. Faced with sparse aperture (SA) data, we use the minimum entropy based method for range alignment and propose a fast eigenvector based autofocus method for phase adjustment in this paper. The novel autofocus method uses the Lanczos or Arnoldi iteration to compute only the desired eigenvector instead of directly doing time consuming eigen decomposition to the sample covariance matrix. Therefore, the computational complexity can be greatly reduced. Simulated scatterer model and raw data are utilized to prove that the proposal is an efficient autofocus algorithm with a lower computational burden.

title: Microwave Based SAR Technique for Pipeline Inspection Using Autofocus Range Doppler Algorithm
abstract: In non destructive testing and evaluation, the microwave based synthetic aperture radar (SAR) imaging have shown great potential in the detection of defects on buried objects such as pipes. However, due to the pipe curvature and high stand off distance when inspecting an insulated pipe, the useful defect information used to characterize the pipe image is lost as a result of low signal to noise ratio (SNR) resulting in a blurred and unfocused image. In this paper, we proposed a robust microwave based SAR imaging using autofocus range Doppler algorithm (RDA) for the inspection of an insulated pipe. Singular value decomposition is used to mitigate the effect of the insulation layer by removing dominant singular values that characterize the insulation layer, and the autofocus RDA is designed to refocus the SAR image using RDA residual refocusing. The SNR, improvement factor (IF) and squared error are used to evaluate the qualitative image information of the defect on the pipe. Experimental results showed the efficacy of the method in detecting defects on an insulated pipe, in particular, a significant reduction in the noise content of the image compared to the known SAR Omega k algorithm. It was found that the autofocus RDA gave higher values of SNR and IF (3 dB and 6 dB) compared to the Omega k algorithm 1 dB and 2 dB) respectively.

title: Spatial variant contrast maximization autofocus algorithm for ISAR imaging of maneuvering targets
abstract: Dear editor, High resolution inverse synthetic aperture radar (ISAR) imaging is hypersensitive to range and azimuth spatial variant phase errors [1,2] which are usually caused by target's maneuvering motion. Some strategies have been proposed to solve the phase errors problem. Methods based on dominant scatterers phase tracking, such as multiple dominant scatterers synthesis method (MDSS)  can obtain well focused imaging results if there exist dominant scattering centers in practical application. In view of the interference of noise on the dominant scatterers extraction, methods based on an evaluation index of the whole image were proposed, such as the maximum contrast phase autofocus algorithm  However, for these traditional autofocus algorithms above, the phase error model is under the assumption that the phase errors are a function of fast and slow time  losing sight of the spatial variant property of maneuvering targets.

title: Non Contact Dermatoscope with Ultra Bright Light Source and Liquid Lens Based Autofocus Function
abstract: Dermatoscopes are routinely used in skin cancer screening but are rarely employed for the diagnosis of other skin conditions. Broader application is promising from a diagnostic point of view as biopsies for differential diagnosis may be avoided but it requires non contact devices allowing a comparably large field of view that are not commercially available today. Autofocus and color reproducibility are specific challenges for the development of dermatoscopy for application beyond cancer screening. We present a prototype for such a system including solutions for autofocus and color reproducibility independent of ambient lighting. System performance includes sufficiently high feature resolution of up to 30 um and feature size scaling fulfilling the requirements to apply the device in regular skin cancer screening.

title: AutoFocus: Interpreting Attention Based Neural Networks by Code Perturbation
abstract: Despite being adopted in software engineering tasks, deep neural networks are treated mostly as a black box due to the difficulty in interpreting how the networks infer the outputs from the inputs. To address this problem, we propose AutoFocus, an automated approach for rating and visualizing the importance of input elements based on their effects on the outputs of the networks. The approach is built on our hypotheses that (1) attention mechanisms incorporated into neural networks can generate discriminative scores for various input elements and (2) the discriminative scores reflect the effects of input elements on the outputs of the networks. This paper verifies the hypotheses by applying AutoFocus on the task of algorithm classification (i.e. given a program source code as input, determine the algorithm implemented by the program) AutoFocus identifies and perturbs code elements in a program systematically, and quantifies the effects of the perturbed elements on the network's classification results. Based on evaluation on more than 1000 programs for 10 different sorting algorithms, we observe that the attention scores are highly correlated to the effects of the perturbed code elements. Such a correlation provides a strong basis for the uses of attention scores to interpret the relations between code elements and the algorithm classification results of a neural network, and we believe that visualizing code elements in an input program ranked according to their attention scores can facilitate faster program comprehension with reduced code.

title: A Generalized Phase Gradient Autofocus Algorithm
abstract: The phase gradient autofocus (PGA) algorithm has seen widespread use and success within the synthetic aperture radar (SAR) imaging community. However, its use and success has largely been limited to collection geometries where either the polar format algorithm (PFA) or range migration algorithm is suitable for SAR image formation. In this paper, a generalized phase gradient autofocus (GPGA) algorithm is developed that is applicable with both the PFA and backprojection algorithm (BPA) thereby directly supporting a wide range of collection geometries and SAR imaging modalities. The GPGA algorithm is shown to preserve the four crucial signal processing steps comprising the PGA algorithm while alleviating the constraint of using a single scatterer per range cut for phase error estimation, which exists with the PGA algorithm. Moreover, the GPGA algorithm, whether using the PFA or BPA, is shown to yield an approximate maximum marginal likelihood estimate of phase errors having marginalized over unknown complex valued reflectivities of selected scatterers. Simulation and experimental results produced by applying the GPGA algorithm to PFA and BPA SAR images are presented.