Selective denoising in document images using reinforcement learning

Image denoising deals with removal of unwanted noise from images. While there have been many techniques that can be applied to denoise a given input noisy image, the methods process an image in its entirety, assuming that the noise uniformly affects the entire image. For inputs where the noise affects a localised part of the image, applying methods that attempt to denoise the entire image can adversely affect the clean portions. To address this problem, we propose a deep reinforcement learning-based framework aiming to overcome this limitation and achieve better results for images with non-uniformly distributed noise. We propose a two-step procedure that first identifies the noisy patch and then denoises the extracted patch. We use a reinforcement learning-based approach for noise localization and use PixelRL for noise removal. We have prepared a comprehensive dataset specifically for the noise localization problem, and noise patches are induced in clean document images using various noise patterns, such as Gaussian noise, coffee stains, and ink bleeds.