Innovative Dual-Stage Blind Noise Reduction in Real-World Images Using Multi-Scale Convolutions and Dual Attention Mechanisms

The distribution of real noise in images can disrupt the inherent symmetry present in many natural visuals, thus making its effective removal a paramount challenge. However, traditional denoising methods often require tedious manual parameter tuning, and a significant portion of deep learning-driven techniques have proven inadequate for real noise. Moreover, the efficiency of end-to-end algorithms in restoring symmetrical patterns in noisy images remains questionable. To harness the principles of symmetry for improved denoising, we introduce a dual deep learning model with a focus on preserving and leveraging symmetrical patterns in real images. Our methodology operates in two stages. In the first, we estimate the noise level using a four-layer neural network, thereby aiming to capture the underlying symmetrical structures of the original image. To enhance the extraction of symmetrical features and overall network performance, a dual attention mechanism is employed before the final convolutional layer. This innovative module adaptively assigns weights to features across different channels, thus emphasizing symmetry-preserving elements. The subsequent phase is devoted to non-blind denoising. It integrates the estimated noise level and the original image, thus targeting the challenge of denoising while preserving symmetrical patterns. Here, a multi-scale architecture is used, thereby amalgamating image features into two branches. The first branch taps into dilation convolution, thus amplifying the receptive field without introducing new parameters and making it particularly adept at capturing broad symmetrical structures. In contrast, the second branch employs a standard convolutional layer to focus on finer symmetrical details. By harnessing varied receptive fields, our method can recognize and restore image symmetries across different scales. Crucial skip connections are embedded within this multi-scale setup, thus ensuring that symmetrical image data is retained as the network deepens. Experimental evaluations, conducted on four benchmark training sets and 12 test datasets, juxtaposed with over 20 contemporary models based on the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) metrics, underscore our model's prowess in not only denoising but also in preserving and accentuating symmetrical elements, thereby setting a new gold standard in the field.