The invention relates to an FPGA-based low-power-consumption multi-channel heterogeneity image correction method and system. The correction method comprises the following steps: respectively obtainingfirst image data, second image data and third image data; Calculating to obtain a first mean value and a second mean value according to the first image data, and calculating to obtain a third mean value according to the second image data; Calculating according to the first mean value and the second mean value to obtain a bias correction factor; Calculating to obtain a gain correction factor according to the first mean value and the third mean value; And correcting the third image data according to the offset correction factor and the gain correction factor. By means of the correction method,non-uniformity correction of image data can be effectively achieved, the calculation precision is high, and the correction method can adapt to large illumination intensity changes.

FPGA-based low-power-consumption multi-channel heterogeneity image correction method and system

The invention discloses an infrared image non-uniformity correction method, which is used for processing an infrared image of a measured object obtained by an acquisition system, and comprises the following steps: step 1, correcting the obtained infrared image by adopting a normalized BP neural network; and step 2, correcting the infrared image corrected in the step 1 by adopting definition improvement based on region attribute classification. On the basis of a traditional artificial neural network, the application of a classic BP neural network algorithm in heterogeneity is optimized from twoaspects, namely a normalization neural network algorithm and a heterogeneity correction algorithm based on regional attribute classification; wherein the normalized BP artificial neural network algorithm aims to eliminate the difference between the order of magnitudes of gain and offset adjustment amount and improve the convergence rate and correction effect of the algorithm, and the latter focuses on improving the definition of the image, so that the efficiency and quality of the algorithm are jointly optimized, and the effect of advantage complementation is achieved.

Infrared image non-uniformity correction method

The invention relates to an infrared image nonuniformity correction method based on a shunt residual network model A noise extraction unit is constructed A non-uniform correction convolution neuralnetwork is constructed by noise extraction unit The non-uniformity correction convolution neural network is trained and the trained non-uniformity correction convolution neural network is obtained The preset image is input into the trained non-uniform correction convolutional neural network to obtain gain correction parameters and offset correction parameters The corrected preset image is obtained by preset image, gain correction parameter and offset correction parameter The correction method provided by the invention effectively estimates gain correction parameters and offset correction parameters, and uses the parameters to correct the infrared image, which can effectively adapt to the non-uniform drift and restrain the ghost phenomenon The corrected image has less non-uniformity residue, higher accuracy, richer image details and sharper visual effect

An infrared image nonuniformity correction method based on a shunt residual network model

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An infrared image nonuniformity correction method based on a shunt residual network model