We present a deep learning method for single image super-resolution (SISR). The proposed approach learns end-to-end mapping between low-resolution (LR) images and high-resolution (HR) images. The mapping is represented as a deep convolutional neural network which inputs the LR image and outputs the HR image. Our network uses 5 convolution layers, which kernels size include 5×5, 3×3 and 1×1. In our proposed network, we use residual-learning and combine different sizes of convolution kernels at the same layer. The experiment results show that our proposed method performs better than the existing methods in reconstructing quality index and human visual effects on benchmarked images.
The further research of visual processing mechanism provides a new idea for contour detection. On the primary visual cortex, the non-classical receptive field of the neurons has the orientation selectivity exerts suppression effect on the response of classical receptive field, which influences edge or line perception. Based on the suppression property of non-classical receptive field and contour completion, this paper proposed a contour detection method based on brightness and contour completion. The experiment shows that the proposed method can not only effectively eliminate clutter information, but also connect the broken contour points by taking advantage of contour completion.
Currently there is no algorithm which can be adapted to all of the imaging conditions. So, it is necessary for us to
find a method to evaluate the existing ATR (automatic target recognition) algorithm. We do some researches on ATR
algorithm performance evaluation based on test methodology. The basic idea of the algorithm performance evaluation is
to establish the relationship model between the image quality characteristics and the algorithm’s performance. In this
paper, the algorithm performance evaluation’s techniques are studied, which include the algorithm performance
assessment framework, the universal test image database’s creating, and the research of the image quality evaluation
model. Firstly, under the guidance of the orthogonal experimental design method, we construct a universal test image
database which includes the simulation image and the outfield flight data. And then this paper propose a method to
establish the relation model between image quality characteristic and ATR algorithm based on SVM classifier. Finally
we use the model to evaluate algorithm’s performance. We conduct experiments on the matching algorithm’s
performance evaluation. The experimental results show that the proposed evaluation framework is efficient and the
evaluation model is well.
Target recognition in natural scenes generally uses the remote sensing image preparation for the matching feature template. Image match system finds correspondence between real image and remote sensing image and then output the target position parameters to the aircraft. How to quantitative analysis and evaluate the robustness of the infrared features, and to determine the available features in infrared image matching recognition algorithm is one of the keys of the ground target recognition in complex scenes. In this paper, we built a feature robustness evaluation model for typical match identifying by analyzing for the robustness of features extracted from typical terrains and targets in various condition. Combined with France
SE-Workbench-IR simulation platform, we designed a special scene simulation development process, in case of lack of terrain generation module, it also can generate MWIR natural scene image. By analyzing the simulation image and real-time image in the same condition, we can gain the variation information from infrared radiation characteristic in different natural condition. Finally, we verify and assess the robustness of the matching features.
The 8-bit PIC core MCU SOC chip integrates three main functions in a single chip providing optimal solutions for various fiber module applications. The functional blocks main include a 2.5Gbps Post-Amplifier (PA), a 1.25Gbps burst or continuous mode Laser-Driver (LD), and an 8-bit PIC-core MCU. The PA receives differential signal and performs the quantization amplifications. The LD driver performs the transmit function. The on-chip MCU is the flexibility and versatility of controlling and configurations of fiber module design. The main purpose of the article is that the system digital peripherals design, such as I2C slave for host communication and its associated SFP 2-port RAM, two levels of password protection for SFP 512-byte registers, and I2C master for EEPROM interface, and the access to on-chip 2K program SRAM, and boot ROM code process.
With the advent of information age, especially with the rapid development of network, "information explosion"
problem has emerged. How to improve the classifier's training precision steadily with accumulation of the samples is the
original idea of the incremental learning. Support Vector Machine (SVM) has been successfully applied in many pattern
recognition fields. While its complex computation is the bottle-neck to deal with large-scale data. It's important to do
researches on the SVM's incremental learning. This article proposes a SVM's incremental learning algorithm based on
the filtering fixed partition of the data set. This article firstly presents "Two-class problem"s algorithm and then
generalizes it to the "Multiclass problem" algorithm by the One-vs-One method. The experimental results on three types
of data sets' classification show that the proposed incremental learning technique can greatly improve the efficiency of
SVM learning. SVM Incremental learning can not only ensure the correct identification rate but also speedup the training
Confidence evaluation is an important technique in image matching process. This paper proposes a confidence level
evaluation method for image matching result based on support vector machine (SVM). We divide the matching result
into two different types: the correct result and the wrong result. So we translate the match result's confidence evaluation
problem into the matching result's classification. This paper firstly provides a method of how to prepare the character
parameters which can accurately reflect the matching performance. And then the SVM based on Gaussian kernel is used
as a classifier to classify the match result and discriminate the match result's type. The experiments show that this
method is effective. Compared with the Dempster-Shafer (D-S) evidence reasoning fusion method it has much higher