Use the teaser and architecture figures from the code repository README

README.md

@@ -27,7 +27,7 @@ Sparse-view Computed Tomography (CT) reconstructs images from a limited number o
 
 We propose Computed Tomography neural Operator (CTO), the first neural operator (NO) framework for CT reconstruction. CTO extends learning from fixed discretized grids to continuous function space, enabling a single model to generalize across measurement sampling rates without retraining. We also propose new NO architectural designs for CT: (i) a dual-domain NO architecture in both sinogram and image spaces, capturing complementary spatial–frequency information, and (ii) rotation-equivariant DIScrete–COntinuous convolutions (DISCO) that exploit the rotational structure inherent in tomographic acquisition. Empirically, CTO outperforms CNNs (> 3.4dB PSNR gain) and other baselines in multi-resolution settings across multiple CT datasets. Compared to state-of-the-art diffusion methods, CTO has 500× faster inference with an average 3dB gain. CTO further demonstrates strong out-of-distribution robustness, maintaining gains under cross-dataset transfer and noisy sinogram conditions. Ablation studies also validate each design choice. CTO establishes neural operators as a principled and practical paradigm for flexible, discretization-agnostic CT reconstruction.
 
-![A single unified neural operator across sampling rates and output resolutions](./assets/overview.png)
+![Teaser image](./assets/teaser.png)
 
 ![CTO architecture](./assets/architecture.png)