Denoising of Fundus Images Using Feed-Forward Convolutional Neural Networks

The biomedical image denoising method has developed into one of the most fascinating study fields today. Every day, lots of biomedical images are taken, and it is from these images that diseases have been diagnosed. To diagnose eye-related diseases, a fundus image is taken. Early detection of eye-related illnesses is essential to prevent severe problems like cataracts and blindness in the future. The analytical procedure is hampered by a noisy fundus, which makes diagnosis difficult. As a result, it's necessary to lessen noise without sacrificing image quality (blood vessels, optic disk, macula, hemorrhage, and exudate). Therefore, classical denoising methods, transform denoising technique and Deep Learning based denoising method have been utilized to diminish these noises and results have been compared based on structural similarity Index matrix (SSIM) and peak signal to noise ratio (PSNR). Finally, Feed-forward Denoising Convolutional Neural Networks (DnCNN) technique outperforms over all others conventional as well as Deep Learning (SDCDAE, GAN-CT, RED-CNN, and CNN-DWT) denoising methods. In DnCNN Residual learning and Batch normalization (RL and BN) have been utilized to accelerate task of training in addition boosts the denoising performance on the same time, preservers the important features of images. DnCNN technique has been provided better results on the specified noise levels as well as unknown noise levels.