Abstract: 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 filtering, grayscale transformations,etc. ,which can lead to significant loss of detail in the image,greatly affecting image interpretation. In recent years,deep learning methods have gradually been applied to image enhancement processing. However,this method 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 subjected 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 images 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 super- resolution reconstruction convolutional neural network,the proposed algorithm has a good advantage in terms of image visual 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.

Key words: remote sensing image of mining area,discrete wavelet transform,deep learning,image enhancement