Huazhong University of Science and Technology

Luxin Yan

Nong Sang

Changxin Gao

Shiwei Zhang

Lerenhan Li

Jin Wang

Yuanjie Shao

Juncai Peng

Hao Li

Tianxu Zhang

Yuehuan Wang

Hao Yan

Bin Lu

Zhixiong Pi

Shiying Gao

Sheng Zhong

Pengxian Yu

The present invention discloses a method and system for selecting an image region that facilitates blur kernel estimation, in which the method includes: calculating a relative total variation value of each pixel in a blurred image to obtain a relative total variation mapping image; setting a threshold value to determine whether respective pixel in the image is a boundary pixel or not; then sampling the blurred image and its relative total variation mapping image to obtain a series of image patches; and finally counting the number of boundary pixels in each mapping image patch and selecting out an image region that facilitates blur kernel estimation. According to the method and the system, the problems of excessive dependency on operator experience, low efficiency and the like in the existing region selection methods are effectively solved. The image region that facilitates blur kernel estimation is automatically selected out. And the method and the system are suitable for the application occasion of the blur kernel estimation in an image deblurring algorithm.

Method and system for selecting image region that facilitates blur kernel estimation

A method for estimating a blur kernel size, the method including: (1) pre-processing a blurred image, to obtain an image, so that a size of the image satisfies an image input size of a multi-class convolutional neural network (CNN); (2) inputting the image into a multi-class CNN with completed training, to obtain a blur-kernel-size probability distribution vector; and (3) comparing each element in the blur-kernel-size probability distribution vector, so that an estimated blur kernel size of the blurred image is the blur kernel size corresponding to a largest element. The invention also provides a system for estimating a blur kernel size. The system includes an image pre-processing module, a training-set synthesizing module, a multi-class CNN module, and a blur kernel size estimation module.

Method and system for estimating blur kernel size

The invention discloses a method for correcting aero-optical thermal radiation noise, comprising steps of: pretreating a degraded image to obtain a multi-scale degraded image group, conducting iteration process of obtaining an optimal solution by using a last scale estimation result as an original value of next scale estimation according to the multi-scale degraded image group, thereby facilitating original-scale bias field estimation, and restoring the degraded image according to the original-scale bias field estimated value thereby obtaining an image after aero-optical thermal radiation noise correction. The invention also discloses a system for correcting aero-optical thermal radiation noise. The invention is capable of solving problems with conventional methods, comprising poor correction effect, high complexity, and incapability in correcting the thermal radiation noise at an image level, and applicable to restoration of an image with aero-optical thermal radiation noise.

Method and system for correcting aero-optical thermal radiation noise

An aircraft-based infrared image recognition device for a ground moving target, including an infrared non-uniformity correction module, an image rotation module, an image registration module, a multi-level filtering module, a connected domain labeling module, a target detection and feature recognition module, a process control module, and a FPGA-based interconnection module. The invention uses an ASIC/SoC chip for image processing and target recognition, the DSP processor and the FPGA processor, it is possible to enable a multi-level image processing and target recognition algorithm, to improve system parallel, and to facilitate an aircraft-based infrared image recognition method for a ground moving target. Meanwhile, embodiments of the invention effectively reduce power consumption of the device.

Aircraft-based infrared image recognition device for ground moving target

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Hubei, China

Luxin Yan