Mining Remote Sensing Image Enhancement Algorithm Based on Deep Residual Learning in Discrete Wavelet Domain
Enhancing remote sensing images of mining areas can significantly improve subsequent image interpretation and monitoring analysis efficiency.Traditional methods for enhancing remote sensing images in mining areas often involve filte-ring,grayscale transformations,etc.,which can lead to significant loss of detail in the image,greatly affecting image interpreta-tion.In recent years,deep learning methods have gradually been applied to image enhancement processing.However,this meth-od heavily relies on model design and parameter tuning,requiring a large number of experiments and optimizations to achieve desirable results.Combining deep learning(DL)with discrete wavelet transform(DWT),a mining area remote sensing image enhancement algorithm based on deep residual learning in the discrete wavelet domain is proposed.Firstly,the image is subjec-ted to single-level 2D discrete wavelet transform to obtain 4 subbands.Then,the coefficients of the 4 subbands are input into a deep residual network to predict corresponding residual images.These residual images are added to the original 4 subband ima-ges to create new subbands for the 2D wavelet transform.Finally,the enhanced image is obtained through 2D inverse discrete wavelet transform.The test results show that:compared with methods such as histogram equalization wavelet transform and su-per-resolution reconstruction convolutional neural network,the proposed algorithm has a good advantage in terms of image visu-al effect,peak signal-to-noise ratio,structural similarity,mean square error and other evaluation indicators,reflecting that the combination of discrete wavelet transform and deep learning is helpful to improve the visual effect of remote sensing images in mining areas and facilitate subsequent image interpretation and interpretation.
remote sensing image of mining areadiscrete wavelet transformdeep learningimage enhancement
NETL
NSTL
万方数据
维普
