During space reconnaissance applications, edge detection from remote sensing imagery plays an important role in the target recognition processing. However, traditional edge detection methods usually only utilize the high-frequency information in one image. Since low-frequency elements may be aliasing with high-frequency parts, the edges extracted may be unconnected under complex topography, different objects and imaging conditions. This paper proposes a novel image edge detection method based on Non-Subsampled Contourlet Transform (NSCT) to keep the object boundary continuously. It transforms the image into Contourlet domain in both high-frequency and low-frequency sub-bands respectively. Depending on the feature of flexible directivity reservation of an image during NSCT, the further edge extraction consists of 3 steps: firstly, the elements of the high-frequency coefficient matrix in Contourlet domain are filtered with high values left using adaptive thresholds. Then the low-frequency edge information is extracted via Canny operator from the low-frequency sub-band information. Finally, to achieve a more consistent edge image, the low-frequency edge image is achieved according to the low-frequency matrix and adopted to compensate the high-frequency image with the isolated noise points eliminated as well. The numerical simulation and practical test results show the higher effectiveness and robustness of the proposed algorithm when comparing with the classical edge detectors, such as Sobel operator, Canny operator, Log operator and Prewitt operator, etc.
In traditional image resizing theory based on interpolation, the prominent object may cause distortion, and the image
resizing method based on content-aware has become a research focus in image processing because the prominent content
and structural features of images are considered in this method. In this paper, we present an optimized fast image
resizing method based on content-aware. Firstly, an appropriate energy function model is constructed on the basis of
image meshes, and multiple energy constraint templates are established. In addition, this paper deducts the image
saliency constraints, and then the problem of image resizing is used to reformulate a kind of convex quadratic program
task. Secondly, a method based on neural network is presented in solving the problem of convex quadratic program. The
corresponding neural network model is constructed; moreover, some sufficient conditions of the neural network stability
are given. Compared with the traditional numerical algorithm such as iterative method, the neural network method is
essentially parallel and distributed, which can expedite the calculation speed. Finally, the effects of image resizing by the
proposed method and traditional image resizing method based on interpolation are compared by adopting MATLAB
software. Experiment results show that this method has a higher performance of identifying the prominent object, and the
prominent features can be preserved effectively after the image is resized. It also has the advantages of high portability
and good real-time performance with low visual distortion.