AKDT: Adaptive Kernel Dilation Transformer for Effective Image Denoising

Image denoising is a fundamental yet challenging task, especially when dealing with high-resolution images and complex noise patterns. Most existing methods rely on standard Transformer architectures, which often suffer from high computational complexity and limited adaptability to varying noise levels. In this paper, we introduce the Adaptive Kernel Dilation Transformer (AKDT), a novel Transformer-based model that fully harnesses the power of learnable dilation rates within convolutions. AKDT consists of several layers and custom-designed blocks, including our novel Learnable Dilation Rate (LDR) module, which is utilized to construct a Noise Estimator module (NE). At the core of AKDT, the NE is seamlessly integrated within standard Transformer components to form the Noise-Guided Feed-Forward Network (NG-FFN) and Noise-Guided Multi-Headed Self-Attention (NG-MSA). These noise-modulated Transformer components enable the model to achieve unparalleled denoising performance while significantly reducing computational costs. Extensive experiments across multiple image denoising benchmarks demonstrate that AKDT sets a new state-of-the-art, effectively handling both real and synthetic noise. The source code and pre-trained models are publicly available at https://github.com/albrateanu/AKDT.