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• A blind spot speckle suppression method based on a global awareness strategy is proposed to achieve self-supervised denoising of sonar images, which helps to reduce the noise interference to downstream tasks. • A hybrid contextual dynamic attention mechanism (HCDAM) is introduced to enhance and correct the locality map for salient region extraction to improve the reliability of segmented targets. • A co-training method for the joint speckle suppression task and the garbage instance segmentation task is presented, which facilitates the flexible execution of underwater garbage detection tasks. Sonar is widely used in marine water cleaning tasks, so sonar images have become an effective tool for garbage detection and underwater scene analysis. However, it is an extremely difficult task to achieve fully supervised denoising and garbage detection for sonar images. This is because sonar images are weakly annotated samples that are susceptible to noise interference and have no reference clean image. To this end, we propose a sonar image garbage instance segmentation model via global despeckling and dynamic attention graph optimization(GD-DAGO). Specifically, a self-supervised blind spot network denoising structure is presented in this paper. The proposed denoising model overcomes the defects of information loss in the traditional blind spot network structure and performs global awareness speckle suppression for the noise characteristics of sonar images themselves. In addition, a novel dynamic attention structure is employed to improve the target region estimation in the instance segmentation module and does not require supervision beyond image-level category labeling. Finally, in order to enhance the cooperative ability between the two tasks, we adopt a local perceptual loss strategy based on mask proposals guided by the downstream task, so that the whole model takes more into account the characteristics of sonar images and better serves the sonar garbage detection task. Experimental results on ARACATI 2017 and marine-debris-fls-datasets (MDFD) show that the proposed algorithm achieves a performance gain of 0.4218 and 4.2% in terms of denoising effect (ENL) and detection accuracy ( AP 25 ), respectively, compared with suboptimal algorithms. [ABSTRACT FROM AUTHOR]