Patch-Craft Self-Supervised Training for Correlated Image Denoising
This addresses the challenge of removing correlated noise in images where traditional supervised methods fail due to lack of paired data, though it is incremental as it builds on existing self-supervised techniques.
The paper tackles the problem of image denoising with unknown correlated noise by proposing a self-supervised training method that uses bursts of noisy shots to create artificial targets, achieving competitive results without ground truth data.
Supervised neural networks are known to achieve excellent results in various image restoration tasks. However, such training requires datasets composed of pairs of corrupted images and their corresponding ground truth targets. Unfortunately, such data is not available in many applications. For the task of image denoising in which the noise statistics is unknown, several self-supervised training methods have been proposed for overcoming this difficulty. Some of these require knowledge of the noise model, while others assume that the contaminating noise is uncorrelated, both assumptions are too limiting for many practical needs. This work proposes a novel self-supervised training technique suitable for the removal of unknown correlated noise. The proposed approach neither requires knowledge of the noise model nor access to ground truth targets. The input to our algorithm consists of easily captured bursts of noisy shots. Our algorithm constructs artificial patch-craft images from these bursts by patch matching and stitching, and the obtained crafted images are used as targets for the training. Our method does not require registration of the images within the burst. We evaluate the proposed framework through extensive experiments with synthetic and real image noise.