DENOISING CONVOLUTIONAL NEURAL NETWORK WITH ENERGY-BASED ATTENTION FOR IMAGE ENHANCEMENT

In the realm of image denoising, the use of convolutional neural networks (CNNs) has lately gained traction. Several activities involve the utilization of excellent-clarity pictures and recordings. Images were captured in a wide variety of illumination circumstances, which means that not all of them are of the highest quality. Low-light photography suffers from a decline in perceived image quality because of the restricted dynamic range of the pixel values. Therefore, it is vital to enhance the appearance of images. Maximum texture retention is achieved by the structural similarity index-loss-based method. The suggested discrete wavelet transform (DWT)-self attention (SA)-Denoising convolutional neural networks (DnCNNs) make use of state-of-the-art techniques for image denoising like energy band analysis, very deep architecture, learning algorithms, dense-sparse-dense training, and regularization approaches. DnCNN is intended to remove the hidden layers' latent, yielding a pure picture. After a degraded input sample has had its relevant energy features retrieved using DWT, the perfect image enhancement is achieved thanks to the incorporation of the self-attention mechanism. Second, a hierarchical-branch network is formed by combining the suggested network with the denoising CNN and additional loss in order to reduce the reliance on the amount of noisy data in multi-modal picture analysis and make the problem of image enhancement more tractable. In the end, DWT-SA-DnCNN's self-learning qualities are used to improve image quality by obtaining features including undesirable noisy data, edge factor, texture, uniform and non-uniform areas, smoothness, and object structure. Simulation results show that our hybrid DWT-SA-DnCNN-based contrast enhancement strategy outperforms state-of-the-art methods.