Sliding DCT Wiener Denoising with Lightweight Residual Refinement
Year: 2026
Type: conference-paper
Abstract: Sliding-window image denoising in the discrete cosine transform (DCT) domain, combining hard-thresholding with empirical Wiener shrinkage, remains attractive due to its computational efficiency, analytical transparency, and strong baseline performance. This paper revisits the classical sliding DCT Wiener denoising framework, with particular emphasis on stabilized overlap-add reconstruction to improve robustness across overlapping blocks and diverse image content.Building on this analytical backbone, we introduce a lightweight hybrid refinement strategy in which a compact convolutional neural network augments the Wiener estimate. The network predicts a residual correction together with a spatially adaptive, noise-aware gating function that selectively controls the refinement strength. This design preserves the stability and interpretability of the Wiener estimator. Experiments on standard benchmark datasets demonstrate consistent improvements in PSNR and SSIM over strong DCT-based baselines and competitive CNN denoisers, with gains that increase at higher noise levels.
Institution: Tampere University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7202366417