2026-10-15T02:04:56Z https://www.scidb.cn/oai

10.57760/sciencedb.j00240.00050 2025-11-13T11:01:44Z

2025-11-13 Sound2Center doi:10.57760/sciencedb.j00240.00050 en The adaptive masking component is the cornerstone of the proposed algorithm，enabling segmented processing of coal mine images. While dividing each image into smaller blocks，the module operates by sequentially applying a mask to the edge and corner pixels of each block, deliberately excluding the central pixels. Subsequently once the masking process is complete， the mask integration module is employed. This module is responsible for fusing the neural network’s output with the masked areas to reconstruct a coherent and denoised image. Furthermore， this module incorporates a quality evaluation mechanism to assess the effectiveness of the integration. The final component of the algorithm is an adaptive integrated loss function， which guides the model during training. This loss function is specifically designed to address the unique challenges of coal mine image denoising， including complex noise patterns and the need to preserve subtle image details. Additionally，by incorporating the original noisy image， the loss function increases the model’s sensitivity to signal changes，enhancing its adaptability across various denoising scenarios and noise conditions. self-supervised learning; mine image denoising; adaptive masking; Hadamard product; adaptive integrated loss 刘健涛 张亚茹 许国庆 郝定溢 RESTRICTED dataset Science Data Bank