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Recently, denoising methods based on supervised learning have exhibitedpromising performance. However, their reliance on external datasets containingnoisy-clean image pairs restricts their applicability. To address thislimitation, researchers have focused on training denoising networks usingsolely a set of noisy inputs. To improve the feasibility of denoisingprocedures, in this study, we proposed a single-image self-supervised learningmethod in which only the noisy input image is used for network training. Gatedconvolution was used for feature extraction and no-reference image qualityassessment was used for guiding the training process. Moreover, the proposedmethod sampled instances from the input image dataset using Bernoulli samplingwith a certain dropout rate for training. The corresponding result was producedby averaging the generated predictions from various instances of the trainednetwork with dropouts. The experimental results indicated that the proposedmethod achieved state-of-the-art denoising performance on both synthetic andreal-world datasets. This highlights the effectiveness and practicality of ourmethod as a potential solution for various noise removal tasks.

Self2Self+: Single-Image Denoising with Self-Supervised Learning and Image Quality Assessment Loss