# Conditional Diffusion Model

In preoperative multimodal cataract data, SLO images are often missing, and prior methods mostly suppressed the effect of the missing modality during fusion or filled it with zeros rather than explicitly recovering a task-relevant representation for it. This work treats missing SLO as proxy representation recovery in latent feature space: OCT image features and clinical text features condition a diffusion model whose reverse denoising estimates a sample-specific SLO proxy representation, which then enters the fusion prediction network. On 2,141 preoperative samples with a 58.80% SLO missing rate, the method reached 0.9714 accuracy under an absolute error within 0.1 LogMAR and a mean absolute error of 0.0423, versus 0.9625 and 0.0457 for the UML backbone baseline.

In preoperative multimodal cataract data, SLO images are often missing, and prior methods mostly suppressed the effect of the missing modality during fusion or filled it with zeros rather than explicitly recovering a task-relevant representation for it. This work treats missing SLO as proxy representation recovery in latent feature space: OCT image features and clinical text features condition a diffusion model whose reverse denoising estimates a sample-specific SLO proxy representation, which then enters the fusion prediction network. On 2,141 preoperative samples with a 58.80% SLO missing rate, the method reached 0.9714 accuracy under an absolute error within 0.1 LogMAR and a mean absolute error of 0.0423, versus 0.9625 and 0.0457 for the UML backbone baseline.

Climate simulations are costly and biased, while correcting them too strongly toward observations risks erasing structural differences among models. A conditional diffusion model trained on seventeen CMIP6 models and ERA5 reanalysis steers each emulated model distribution toward observations at sampling time with an adjustable strength, chosen in perfect-model experiments with withheld simulated futures and decreasing with projection horizon and forcing. Across seventeen synthetic targets, guidance more than halves median biases in the 1st and 99th percentiles of daily temperature and brings CMIP6's roughly threefold historical overestimate of extreme-heat coverage close to observations.

Climate simulations are costly and biased, while correcting them too strongly toward observations risks erasing structural differences among models. A conditional diffusion model trained on seventeen CMIP6 models and ERA5 reanalysis steers each emulated model distribution toward observations at sampling time with an adjustable strength, chosen in perfect-model experiments with withheld simulated futures and decreasing with projection horizon and forcing. Across seventeen synthetic targets, guidance more than halves median biases in the 1st and 99th percentiles of daily temperature and brings CMIP6's roughly threefold historical overestimate of extreme-heat coverage close to observations.

γ-photon flow-field images reconstructed from limited coincidence events have blurred boundaries and local structures that noise can swamp; a conventional conditional diffusion model injects the degraded image only by concatenation, so the structural prior is diluted through repeated downsampling, and its frequency processing does not change with the diffusion step. The Structure-Frequency Guided Diffusion (SFGD) model feeds edge and morphology priors into the denoiser throughout via a multi-scale structure stream and windowed cross-attention, and tunes the range and gain of frequency enhancement by diffusion timestep. On a phantom-level independent test set, the full model reaches 28.325 dB PSNR and 0.926 SSIM, 5.499 dB and 0.153 above the conditional diffusion baseline.

γ-photon flow-field images reconstructed from limited coincidence events have blurred boundaries and local structures that noise can swamp; a conventional conditional diffusion model injects the degraded image only by concatenation, so the structural prior is diluted through repeated downsampling, and its frequency processing does not change with the diffusion step. The Structure-Frequency Guided Diffusion (SFGD) model feeds edge and morphology priors into the denoiser throughout via a multi-scale structure stream and windowed cross-attention, and tunes the range and gain of frequency enhancement by diffusion timestep. On a phantom-level independent test set, the full model reaches 28.325 dB PSNR and 0.926 SSIM, 5.499 dB and 0.153 above the conditional diffusion baseline.

High-resolution Adriatic ocean reanalysis fields previously came from bilinear interpolation or costly nested regional models, and interpolation smooths away fronts and mesoscale structure. OcDiffSR applies a conditional denoising diffusion probabilistic model to jointly super-resolve sea-surface temperature, salinity, and horizontal velocity, using 1/12° GLORYS12V1 fields as conditioning input to reconstruct 1/24° Med MFC fields. On the independent test year 2009, scalar-field errors fall well below interpolation and the residual diffusion model CorrDiff, with salinity RMSE of 0.346 psu and temperature RMSE of 0.477°C, while velocity correlation stays near 0.64.

High-resolution Adriatic ocean reanalysis fields previously came from bilinear interpolation or costly nested regional models, and interpolation smooths away fronts and mesoscale structure. OcDiffSR applies a conditional denoising diffusion probabilistic model to jointly super-resolve sea-surface temperature, salinity, and horizontal velocity, using 1/12° GLORYS12V1 fields as conditioning input to reconstruct 1/24° Med MFC fields. On the independent test year 2009, scalar-field errors fall well below interpolation and the residual diffusion model CorrDiff, with salinity RMSE of 0.346 psu and temperature RMSE of 0.477°C, while velocity correlation stays near 0.64.

Digital restoration of Dunhuang murals no longer denoises only in pixel space. Pixel-domain diffusion must handle global structure and local brushwork at once, which is costly and can drift in color and style. WDCRF first applies a 2D discrete wavelet transform to split a damaged mural into one low-frequency and three high-frequency subbands, then lets a conditional diffusion model rebuild all four coefficient sets directly in the wavelet domain, using the damaged coefficients themselves as the conditioning signal, and finally synthesizes the image by inverse wavelet transform. On the Dunhuang Mural dataset it reaches 32.04 dB PSNR and 0.922 SSIM, ahead of EdgeConnect, WaveDiff, PGRDiff and LRDiff; on DhMurals it reaches 18.95 dB PSNR and 0.7598 SSIM.

Digital restoration of Dunhuang murals no longer denoises only in pixel space. Pixel-domain diffusion must handle global structure and local brushwork at once, which is costly and can drift in color and style. WDCRF first applies a 2D discrete wavelet transform to split a damaged mural into one low-frequency and three high-frequency subbands, then lets a conditional diffusion model rebuild all four coefficient sets directly in the wavelet domain, using the damaged coefficients themselves as the conditioning signal, and finally synthesizes the image by inverse wavelet transform. On the Dunhuang Mural dataset it reaches 32.04 dB PSNR and 0.922 SSIM, ahead of EdgeConnect, WaveDiff, PGRDiff and LRDiff; on DhMurals it reaches 18.95 dB PSNR and 0.7598 SSIM.