Performance Bounds on Image Denoising or “Is Denoising Dead?”

Image denoising has been a well studied problem in the field of image processing. Yet researchers continue to focus attention on it in order to better the current state of the art. Recently proposed methods take widely different approaches to the problem and yet their denoising performances are comparable. A pertinent question then to ask is whether there is a theoretical limit to denoising performance and more importantly, are we there yet? As camera manufacturers continue to pack increasing numbers of pixels per unit area, we study the performance limits of denoising algorithms that they rely on to reduce the effects of the resultant increase in noise sensitivity. Our work in this paper estimates a lower bound on the mean squared error of the denoised result and compares the performance of current state of the art denoising methods with this bound. We show that despite the phenomenal recent progress in the quality of denoising algorithms, much room for improvement still remains, particularly for difficult to denoise images, and at low signal-to-noise levels. Therefore, denoising is not dead – yet.