High Resolution Image Reconstruction Method Based on Sparse Samples

Abstract

The invention discloses a high-resolution image reconstruction method based on sparse samples, belonging to the field of image processing. The method comprises the following realization processes: firstly taking the original low-resolution images as low frequencies and null matrices as high frequencies in three directions to carry out inverse wavelet transformation to complete image initialization, then utilizing contourlet to decompose the initial images, optimizing the coefficients in a decomposition domain, building neighbourhood vectors around the optimized coefficients and finally utilizing the mapping functions of the high/low-resolution sparsity coefficient neighbourhood vectors obtained in the training stage and neighbourhood fusion to obtain high-resolution contourlet coefficients and obtaining the high-resolution reconstructed images through inverse contourlet transformation. The method has the following advantages: the reconstruction accuracy of fine image textures is improved by utilizing the directionality of the contourlet coefficients, the reconstruction speed of the images is improved by utilizing the sparsity of the contourlet coefficients and possible noise jamming is inhibited by optimization of the contourlet coefficients; and through the comparison of several image reconstruction methods, the reconstruction method disclosed by the invention has the advantages of the highest peak signal to noise ratio and the best visual effect.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a high-resolution image reconstruction method based on sparse samples. Background technique [0002] During the image acquisition process, due to the performance of the imaging sensor and the influence of external factors in the image transmission process, the captured images are often of low resolution, which directly affects the subsequent image-based processing and analysis work. The most direct way to improve the accuracy of the image measurement system is to increase the resolution of the CCD camera, that is, to increase the number of pixels, but the cost of improving the detection accuracy by increasing the hardware resolution is quite expensive, so people hope to use digital image processing technology to improve the original The resolution of the image leads to the problem of high-resolution image reconstruction. At present, the methods to solve this kind of pro...

AI Technical Summary

Problems solved by technology

The spatial domain method is greatly affected by noise, and the traditional frequency domain high-pass method loses the time (spatial) information of the original image signal. The wavelet-based method can focus on any details of the signal through the localized analysis of the time (spatial) frequency, but Since the two-dimensional separable wavelet transform is formed by the one-dimensional wavelet through the tensor product, the isotropic nature of the basis function leads to poor direction selection and cannot effectively capture the contour information, thus restricting the development of wavelet-based image reconstruction methods

Images