Date of Graduation


Document Type

Problem/Project Report

Degree Type



Statler College of Engineering and Mineral Resources


Lane Department of Computer Science and Electrical Engineering

Committee Chair

Xin Li

Committee Member

Roy Nutter

Committee Member

Brian Woerner


Image denoising is an important problem in image processing and computer vision. In real-world applications, denoising is often a pre-processing step (so-called low-level vision task) before image segmentation, object detection, and recognition at higher levels. Traditional image denoising algorithms often make idealistic assumptions with the noise (e.g., additive white Gaussian or Poisson). However, the noise in the real-world images such as high-ISO photos and microscopic fluorescence images are more complex. Accordingly, the performance of those traditional approaches degrades rapidly on real-world data. Such blind image denoising has remained an open problem in the literature.

In this project, we report two competing approaches toward blind image denoising: supervised and unsupervised learning. We report the principles, performance, differences, merits, and technical potential of a few blind denoising algorithms.

Supervised learning is a regression model like CNN with a large number of pairs of corrupted images and clean images. This feed-forward convolution neural network separates noise from the image. The reason for using CNN is its deep architecture for exploiting image characteristics, possible parallel computation with modern powerful GPU’s and advances in regularization and learning methods to train. The integration of residual learning and batch normalization is effective in speeding up the training and improving the denoising performance. Here we apply basic statistical reasoning to signaling reconstruction to map corrupted observations to clean targets

Recently, few deep learning algorithms have been investigated that do not require ground truth training images. Noise2Noise is an unsupervised training method created for various applications including denoising with Gaussian, Poisson noise. In the N2N model, we observe that we can often learn to turn bad images to good images just by looking at bad images. An experimental study is conducted on practical properties of noisy-target training at performance levels close to using the clean target data. Further, Noise2Void(N2V) is a self-supervised method that takes one step further. This is method does not require clean image data nor noisy image data for training. It is directly trained on the current image that is to be denoised where other methods cannot do it. This is useful for datasets where we cannot find either a noisy dataset or a pair of clean images for training i.e., biomedical image data.