||Residual Learning of Transfer-learned AlexNet for Image Denoising
||Mohan Laavanya;Veeramani Vijayaraghavan
||AlexNet; Deep learning; Image denoising; Residual learning; Transfer learning
||In today’s scenarios, deep learning has fascinated all researchers from numerous arenas who developed ways to achieve obligatory outcomes. In deep learning, transfer learning is undergoing deep study, because the study helps to practice a pre-trained network for our own tasks. A novel, transfer-learned AlexNet-based residual learning for Gaussian noise reduction is presented in this paper. The method can remove any level of Gaussian noise without having information about the noise variance in both gray scale and color images. Therefore, our technique is blind Gaussian image denoising that learns a residual image by eradicating the clean image from the transfer-learned AlexNet, and removes noise by identifying the difference from the input image. Experimental results with the proposed scheme are compared against a Gaussian denoiser for image denoising in terms of peak signal-to-noise ratio (PSNR) and visual perception. The results have revealed that our residual learning using transfer-learned AlexNet attains promising denoising results.