Active seismic imaging is used to infer subsurface structure from reflected wavefields, but acquisition and ambient noise can obscure weak reflectors and reduce interpretation reliability. Seismic denoising must remove noise while keeping geological structure intact. This thesis studies a parameter-efficient method to adapt pretrained vision foundation models to this task. The method treats each seismic section as a 2D grayscale image, maps it into a format compatible with vision backbones, and applies Low-Rank Adaptation (LoRA) to limit the number of trainable parameters. It then combines the denoised outputs of multiple adapted vision models through a learned soft spatial blender. This blender merges the expert predictions at the pixel level, allowing the final model to use complementary architectural strengths such as multiscale representation and long-range dependencies. The method is evaluated against a seismic foundation model baseline, using both quantitative metrics and qualitative inspection. Across 25 seed/split repetitions, the residual joint spatial blender achieves a mean absolute error of 0.0463, a peak signal-to-noise ratio of 33.98\,dB, and a structural similarity index of 0.9727, substantially outperforming the standalone adapted experts and the frozen baseline. These results show that jointly trained spatial fusion improves seismic denoising performance while keeping training parameter-efficient. ... Read more