mlsanityData to 7 Oct 2026

Synergy Between Semantic Segmentation and Image Denoising via Alternate Boosting

arXiv 2102.12095 ↗First posted 24 Feb 2021cs.CVcited by 3

Not on the map. Why?

Abstract (arXiv)

The capability of image semantic segmentation may be deteriorated due to noisy input image, where image denoising prior to segmentation helps. Both image denoising and semantic segmentation have been developed significantly with the advance of deep learning. Thus, we are interested in the synergy between them by using a holistic deep model. We observe that not only denoising helps combat the drop of segmentation accuracy due to noise, but also pixel-wise semantic information boosts the capability of denoising. We then propose a boosting network to perform denoising and segmentation alternately. The proposed network is composed of multiple segmentation and denoising blocks (SDBs), each of which estimates semantic map then uses the map to regularize denoising. Experimental results show that the denoised image quality is improved substantially and the segmentation accuracy is improved to close to that of clean images. Our code and models will be made publicly available.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 44 of its 76 references that are in the map. First posted on arXiv 24 Feb 2021.

Builds on

44 of 76 references.

References · 44

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32 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 0; 2026: 2.

3 papers.

  1. 2023Multi-Content Interaction Network for Few-Shot Segmentationcited by 2
  2. 2026ImPartial: Multi-channel Whole-Cell Segmentation using Partial Annotationscited by 0
  3. 2026Joint Medical Image Enhancement and Segmentation with Diffusion-based Symbiotic Information Interactioncited by 0