Assessing Advances in Real Noise Image Denoisers

Recently image denoiser networks have made a number of advances to go beyond additive Gaussian white noise and deal with real noise, such as produced by digital cameras. We note that some of the performance gains reported in the state of the art could potentially be explained by an increase of network sizes. In this paper we propose to revisit some of these advances, including the synthetic noise generator and noise maps proposed in CBDNet, and re-assess them using a simple DnCNN baseline network and thus attempt at measuring how much gains can be attributed to using more modern architectures. In this work, we observe an increase of over +2 dB in denoising performance over our baseline network on the DND real world benchmark. Through this observation, we demonstrate that a smaller networks can offer competitive denoising results when correctly optimised for real world denoising.