  Blind-spot networks (BSN) have been prevalent network architectures in
self-supervised image denoising (SSID). Existing BSNs are mostly conducted with
convolution layers. Although transformers offer potential solutions to the
limitations of convolutions and have demonstrated success in various image
restoration tasks, their attention mechanisms may violate the blind-spot
requirement, thus restricting their applicability in SSID. In this paper, we
present a transformer-based blind-spot network (TBSN) by analyzing and
redesigning the transformer operators that meet the blind-spot requirement.
Specifically, TBSN follows the architectural principles of dilated BSNs, and
incorporates spatial as well as channel self-attention layers to enhance the
network capability. For spatial self-attention, an elaborate mask is applied to
the attention matrix to restrict its receptive field, thus mimicking the
dilated convolution. For channel self-attention, we observe that it may leak
the blind-spot information when the channel number is greater than spatial size
in the deep layers of multi-scale architectures. To eliminate this effect, we
divide the channel into several groups and perform channel attention
separately. Furthermore, we introduce a knowledge distillation strategy that
distills TBSN into smaller denoisers to improve computational efficiency while
maintaining performance. Extensive experiments on real-world image denoising
datasets show that TBSN largely extends the receptive field and exhibits
favorable performance against state-of-the-art SSID methods. The code and
pre-trained models will be publicly available at
https://github.com/nagejacob/TBSN.
