Worked on the deepinv/deepinv repository to implement complex-valued data support for WaveletDenoiser, WaveletDictDenoiser, and WaveletPrior, enhancing the denoising pipeline for complex signals. Leveraged Python and PyTorch to update internal helper functions and extend the PSNR metric for accurate evaluation of complex data. Focused on numerical computing and signal processing, the work included comprehensive test updates to validate new functionality and prevent regressions. Addressed MRI-related requirements by stabilizing changes and introducing performance checks, ensuring reliable processing of complex-valued inputs. The contribution emphasized stability, measurable improvements in denoising quality, and robust test coverage for advanced wavelet-based methods.