A Destriping Framework With Arbitrary Bounded Image Denoisers

Explicit stripes in digital images have long posed challenges for computer vision tasks, which significantly disturbs visual perceptions and is not desirable for subsequent applications. Existing methods for destriping tasks, often constrained by image prior assumptions and lacking flexibility, demonstrate limited practical applicability. In response to these challenges, this paper proposes a flexible Destriping framework with arbitrary bounded Image Denoisers, called DID. The proposed framework decouples the destriping task into conditional expectation calculation and stripe estimation, and alternates between these two parts, which finally obtains the maximum likelihood estimation of the image. The former calculates the conditional expectation of the clean image given the estimated stripe, while the latter estimates the mean of each column in the residual image. To calculate the conditional expectation, this paper analyzes the equivalence between general image denoising and conditional expectation calculation based on Bayesian statistics. It is proven that the proposed DID framework flexibly incorporates existing denoisers to calculate the conditional expectation, without the need to explicitly define image prior assumptions. Furthermore, the fixed-point convergence of the DID framework is guaranteed postulating that the applied denoiser is bounded in an F-norm manner. Experimental results on both synthesized and real data validate the effectiveness and generalization of the proposed method, both quantitatively and qualitatively.