  Recently, denoising methods based on supervised learning have exhibited
promising performance. However, their reliance on external datasets containing
noisy-clean image pairs restricts their applicability. To address this
limitation, researchers have focused on training denoising networks using
solely a set of noisy inputs. To improve the feasibility of denoising
procedures, in this study, we proposed a single-image self-supervised learning
method in which only the noisy input image is used for network training. Gated
convolution was used for feature extraction and no-reference image quality
assessment was used for guiding the training process. Moreover, the proposed
method sampled instances from the input image dataset using Bernoulli sampling
with a certain dropout rate for training. The corresponding result was produced
by averaging the generated predictions from various instances of the trained
network with dropouts. The experimental results indicated that the proposed
method achieved state-of-the-art denoising performance on both synthetic and
real-world datasets. This highlights the effectiveness and practicality of our
method as a potential solution for various noise removal tasks.
