The invention relates to an adaptive low-illumination visible image and infrared image fusion method, and belongs to the field of digital image processing. The method comprises the steps: image preprocessing, NSCT transformation, frequency domain coefficient fusion and an NSCT inversion. The method mainly achieves the multi-scale decomposition of a bright image and an infrared visible image, extracts high-frequency components and low-frequency components, and selects different fusion rules to carry out the frequency domain fusion according to the characteristics of the high-frequency components and low-frequency components. The NSCT inversion part is mainly used for carrying out the multi-scale inversion of the obtained fusion high-frequency and low-frequency components, obtaining the gray scale image after fusion, then carrying out the weighting of the gray scale image after fusion and an original color visible image, and obtaining a color fusion image. The method can effectively keep more detail information of the original image, can improve the contrast and resolution of the fusion image, and can be widely used in the fields of intelligent traffic, video monitoring, medical diagnosis, target detection and national defense security.

Adaptive low-illumination visible image and infrared image fusion method

The invention relates to a contourlet transformation-adaptive medical image fusion method based on non-sampling and belongs to the field of image processing. The method comprises the steps that firstly, a source image is subjected to arithmetic average filtering and then is decomposed through an orthogonal 9-7 wavelet filter and a pkva filter during non-sampling to obtain low-frequency sub-band coefficients and all band-pass direction sub-band coefficients; secondly, the low-frequency sub-band coefficients are selected and fused according to the edge information maximum criterion, all the band-pass sub-band coefficients are selected and fused through an adaptive PCNN model based on a visual neuron model; lastly, a final fused image is obtained by means of inverse transformation of NSCT. According to the contourlet transformation-adaptive medical image fusion method based on non-sampling, the algorithm is very effective and correct, the edge and space texture information of the fused image is clear, color distortion is low, the false contour phenomenon does not exist, and feature information of the source image is well reserved.

Contourlet transformation-adaptive medical image fusion method based on non-sampling

The invention provides an image fusion method based on compressed sensing. The image fusion method includes the three steps of image collection, observed value fusion and image reconstruction. According to the image collection, an image to be fused is divided into image blocks, according to the observed value fusion, a double-channel pulse coupling neural network model is adopted for performing primary image fusion, and a weighted average method is adopted for performing fine fusion on observed values, and finally a final image fusion result is obtained through an image reconstruction algorithm. In the sampling step, characteristics of the image self to be fused are fully considered, and detail information of the result obtained through the fusion is improved; a partitioning compression method is adopted, the partitioned images are sampled and compressed at the same time at a sampling terminal, and complexity, caused by sparse processing performed in advance, of a traditional compressed sensing sampling terminal is avoided; the reconstruction algorithm is high in reconstruction speed and robustness.

Image fusion method based on compressed sensing

The invention discloses a multi-focus image fusion method based on non-subsampled Contourlet transform and a depth information incentive PCNN (Pulse Coupled Neural Network). The method comprises the following steps of generating a low-frequency sub-band image and a series of high-frequency sub-band images after carrying out multi-scale and multidirectional non-subsampled Contourlet transform on input multi-focus source images; adopting edge information energy based on sub-band coefficients to low-frequency sub-bands to obtain low-frequency sub-band coefficients and adopting a modified PCNN model to high-frequency sub-bands to determine each band-pass sub-band coefficient; and lastly, obtaining a fused image through non-subsampled Contourlet inverse transform. The modified PCNN is mainly embodied in that a factor combined with image depth information through adopting SML (Sum-Modified-Laplacian) capable of describing an image direction and texture information well is taken as input of a model, and most of PCNN-based algorithms take pixel gray values as model input items. The method can be well applied to the field of image fusion, and an experimental result shows that a fusion result which more conforms to an eye vision rule can be provided from both the objective evaluation index and the subjective vision effect.

Multi-focus image fusion method based on NSCT (Non-Subsampled Contourlet Transform) and depth information incentive PCNN (Pulse Coupled Neural Network)

The invention discloses a method for fusing an infrared image and a visible light image. The method comprises the following steps that (1) significance tests are carried out on the input infrared image and the input visible light image; (2) the infrared image and the visible light image are input to a PCNN model as output of external excitation and output of peripheral neurons, and image infusion is carried out on the PCNN model by combining significance test results so that a final image can be obtained. According to the method for fusing the infrared image and the visible light image, the infrared image and the visible light image are input to one PCNN model at the same time, weighting is carried out with significant values as guidance, and therefore the method is an efficient nolinear image fusion method.

Method for fusing infrared image and visible light image

The invention discloses a multi-focus image fusing method based on a dual-channel PCNN (Pulse Coupled Neural Network), which belongs to the technical field of image processing. The method comprises the following steps of: performing NSCT (Non-Subsampled Contourlet Transform) on two images respectively to obtain a plurality of sub-images of different frequencies; fusing by correspondingly adopting the dual-channel PCNN, and determining each band pass sub-band coefficient of a fused image; and performing reverse NSCT to obtain the fused image. Due to the adoption of the multi-focus image fusing method, the defects of the conventional multi-focus image fusing method are overcome, and the fusing effect is improved.

Baohua Zhang

Papers

Multi-focus image fusing method based on dual-channel PCNN (Pulse Coupled Neural Network)