Energy based denoising convolutional neural network for image enhancement

Abstract

ABSTRACT Deep learning technologies like convolutional neural networks have recently become popular in the field of image denoising. A combined discrete wavelet transform (DWT) and denoising CNN (DnCNN)-based technique suggested in this paper can be used to adaptively boost image contrast. Images were captured in a wide variety of lighting circumstances, which means that not all of them are of the highest quality. As a result of the limited dynamic range of the pixel values, the overall image quality suffers when a shot is taken in low light. We employ a lossy method based on a structural similarity index to keep as much of the original texture in the images as feasible. Current advancements in image denoising, such as energy band analysis, very deep architecture, learning algorithms, dense-sparse-dense training, and regularization approaches, were applied in the hybrid DWT-Denoising convolutional neural networks. DnCNN eliminates the latent information in the hidden layers by design, resulting in a clean image. The DWT-DnCNN delivers SSIM values that are 2% higher than those of the standard RIDNet. Our hybrid DWT-DnCNN-based contrast enhancement strategy surpasses existing methods, according to simulation outcomes.