Considering its advantages in dealing with high-dimensional visual input and learning control policies in discrete domain, Deep Q Network (DQN) could be an alternative method of traditional auto-focus means in the future. In this paper, based on Deep Reinforcement Learning, we propose an end-to-end approach that can learn auto-focus policies from visual input and finish at a clear spot automatically. We demonstrate that our method - discretizing the action space with coarse to fine steps and applying DQN is not only a solution to auto-focus but also a general approach towards vision-based control problems. Separate phases of training in virtual and real environments are applied to obtain an effective model. Virtual experiments, which are carried out after the virtual training phase, indicates that our method could achieve 100 accuracy on a certain view with different focus range. Further training on real robots could eliminate the deviation between the simulator and real scenario, leading to reliable performances in real applications.
As described above, existed passive auto-focus techniques contain two separate sections: a focus measure function and a search algorithm. Previous works contributed to these sections separately. Comparative works were done in evaluating different focus measure functions, e.g. Gaussian Based Methods, Laplacian Based Methods, etc., in execution time, correctness and other critical criterias @cite_13 @cite_16 . In this paper, we choose Tenengrad method (one type of Gaussian Based Methods) as a reference for reward due to its balanced performace on execution time and correctness. As for search algorithms, traditional methods focused on finding the nearest peak @cite_3 or the highest peak and all peaks @cite_17 based on intuitive ideas. Recent research utilized neural networks to search and achieved less computational complexity @cite_14 as well as faster speed @cite_7 , but they still applied focus measure values as inputs to feed the neural network.
@cite_14 @cite_7 @cite_3 @cite_16 @cite_13 @cite_17
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Digital cameras are equipped with passive autofocus mechanisms where a lens is focused using only the camera's optical system and an algorithm for controlling the lens. The speed and accuracy of the autofocus algorithm are crucial to user satisfaction. In this paper, we address the problems of identifying the global optimum and significant local optima (or peaks) when focusing an image. We show that supervised machine learning techniques can be used to construct a passive autofocus heuristic for these problems that out-performs an existing hand-crafted heuristic and other baseline methods. In our approach, training and test data were produced using an offline simulation on a suite of 25 benchmarks and correctly labeled in a semi-automated manner. A decision tree learning algorithm was then used to induce an autofocus heuristic from the data. The automatically constructed machine-learning-based (ml-based) heuristic was compared against a previously proposed hand-crafted heuristic for autofocusing and other baseline methods. In our experiments, the ml-based heuristic had improved speed--reducing the number of iterations needed to focus by 37.9 on average in common photography settings and 22.9 on average in a more difficult focus stacking setting--while maintaining accuracy. Recent development of compressive sensing has greatly benefited radar imaging problems. In this paper, we investigate the problem of obtaining enhanced targets such as ships and airplanes, where targets often exhibit structured sparsity. A novel structured sparsity-driven autofocus algorithm is proposed based on sparse Bayesian framework.The structured sparse prior is imposed on the target scene in a statistical manner. Based on a statistical framework, the proposed algorithm can simultaneously cope with structured sparse recovery and phase error correction problem. The focused high-resolution radar image can be obtained by iteratively estimating scattering coefficients and phase. Due to the structured sparse constraint, the proposed algorithm can desirably preserve the target region and alleviate over-shrinkage problem, compared to previous sparsity-driven auto-focus approaches.Moreover, to accelerate convergence rate of the algorithm, we propose to adaptively eliminate noise-only range cells in estimating phase errors. The selection is conveniently conducted based on the parameters controlling sparsity degree of the signal in the proposed hierarchical model.The simulated and real data experimental results demonstrate that the proposed algorithm can obtain more concentrated images with much smaller number of iterations, particularly in low SNR and highly under-sampling scenarios. HighlightsA novel structured sparsity-driven autofocus algorithm is proposed based on sparse Bayesian framework.To accelerate convergence rate of the algorithm, we propose to adaptively eliminate noise-only range cells in phase error estimation stage.The proposed algorithm can obtain more concentrated images with much smaller number of iterations, particularly in low SNR and highly under-sampling scenarios. 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. We present an unified approach to study online scheduling problems in the resource augmentation speed scaling models. Potential function method is extensively used for analyzing algorithms in these models; however, they yields little insight on how to construct potential functions and how to design algorithms for related problems. In the paper, we generalize and strengthen the dual-fitting technique proposed by [1]. The approach consists of considering a possibly non-convex relaxation and its Lagrangian dual; then constructing dual variables such that the Lagrangian dual has objective value within a desired factor of the primal optimum. The competitive ratio follows by the standard Lagrangian weak duality. This approach is simple yet powerful and it is seemingly a right tool to study problems with resource augmentation or speed scaling. We illustrate the approach through the following results. 1 We revisit algorithms EQUI and LAPS in Non-clairvoyant Scheduling to minimize total flow-time. We give simple analyses to prove known facts on the competitiveness of such algorithms. Not only are the analyses much simpler than the previous ones, they also explain why LAPS is a natural extension of EQUI to design a scalable algorithm for the problem. 2 We consider the online scheduling problem to minimize total weighted flow-time plus energy where the energy power f(s) is a function of speed s and is given by s α for α ≥ 1. For a single machine, we showed an improved competitive ratio for a non-clairvoyant memoryless algorithm. For unrelated machines, we give an O(α logα)-competitive algorithm. The currently best algorithm for unrelated machines is O(α 2)-competitive. 3 We consider the online scheduling problem on unrelated machines with the objective of minimizing ∑ i,j w ij f(F j ) where F j is the flow-time of job j and f is an arbitrary non-decreasing cost function with some nice properties. We present an algorithm which is ( 1 1-3 )-speed, ( 2K( ) )-competitive where K(e) is a function depending on f and e. The algorithm does not need to know the speed (1 + e) a priori. A corollary is a (1 + e)-speed, ( k ^ 1+1 k )-competitive algorithm (which does not know e a priori) for the objective of minimizing the weighted l k -norm of flow-time.