报告摘要：In the past decade, much progress has been made in image denoising due to the use of low-rank representation and sparse coding. In the meanwhile, state-of-the-art algorithms also rely on an iteration step to boost the denoising performance. However, the boosting step is fixed or non-adaptive. In this work, we perform rank-1 based fixed-point analysis, then, guided by our analysis, we develop the first adaptive boosting (AB) algorithm, whose convergence is guaranteed. Preliminary results on the same image dataset show that AB uniformly outperforms existing denoising algorithms on every image and at each noise level, with more gains at higher noise levels.