Inconsistent responses of X-ray detector elements lead to stripe artifacts within the sinogram data, which subsequently manifest as ring artifacts in the reconstructed computed tomography (CT) images, severely degrading image quality. This paper presents a novel method for correcting stripe artifacts in the sinogram data by separating the sinogram into an Ideal Sinogram (IS) and Stripe Artifacts (SA), with both components parameterized through Implicit Neural Representations (INR). The proposed method leverages INR to correct defective pixel response values using implicit continuous functions while simultaneously learning stripe features in the angular direction of the sinogram data. These two components, IS and SA, are combined within an optimization constraint framework, achieving unsupervised iterative correction of stripe artifacts in the projection domain. Experimental results demonstrate that the proposed method significantly outperforms current state-of-the-art techniques in effectively removing ring artifacts while maintaining the clarity and fidelity of CT images, thereby enhancing the overall diagnostic quality of CT imaging.

Depth information calculated from the spatial-angular consistent light field data of a Lambertian scene without occlusion provides a Monge surface representation of the scene surface. The light field surface gradient and Laplace operators can then be defined on the moving frame. The light field surface scale space can be established by solving the diffusion equation, and the light field surface feature (LFSF) can be detected. Then the local surface gradients of each LFSF are measured and form a 48 dimensional rotation invariant spherical descriptor. Experimental results show that the proposed method can detect more features then LiFF and Lisad on most of the scenes. For light field data matching, the proposed method provides more matching pairs than LiFF with SIFT descriptor and procides more accurate matching result.