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We propose a novel direction to improve the denoising quality offiltering-based denoising algorithms in real time by predicting the best filterparameter value using a Convolutional Neural Network (CNN). We take the usecase of BM3D, the state-of-the-art filtering-based denoising algorithm, todemonstrate and validate our approach. We propose and train a simple, shallowCNN to predict in real time, the optimum filter parameter value, given theinput noisy image. Each training example consists of a noisy input image(training data) and the filter parameter value that produces the best output(training label). Both qualitative and quantitative results using the widelyused PSNR and SSIM metrics on the popular BSD68 dataset show that theCNN-guided BM3D outperforms the original, unguided BM3D across different noiselevels. Thus, our proposed method is a CNN-based improvement on the originalBM3D which uses a fixed, default parameter value for all images.

CNN-Based Real-Time Parameter Tuning for Optimizing Denoising Filter Performance