Deep Fixed Projector: Fast Projection Network for Image Denoising via Frozen Weights and Inter-Inference Consistency

Unsupervised methods like deep image prior (DIP) leverage network priors for denoising without labeled data but suffer from slow convergence and overfitting, while deep random projector (DRP) improves efficiency via fixed weights and a random seed yet remains limited by its fully random initialization. In this work, we propose deep fixed projector (DFP), an enhanced DRP-based framework featuring three synergistic improvements: (1) initializing the seed with the noisy image to align optimization with the clean image manifold, (2) using pre-trained clean-to-clean encoder-decoder weights to embed structural priors and accelerate convergence, and (3) introducing inter-inference consistency (IIC), a self-supervised regularization that enforces output stability under input perturbations to suppress noise and reduce overfitting. Experiments show DFP consistently surpasses DIP, DRP, and recent variants in PSNR, SSIM, and LPIPS, while achieving faster convergence and robust denoising quality. Code is available athttps://github.com/Hu-China/Deep-Fixed-Projector.