Multi-scale information fusion generative adversarial network for real-world noisy image denoising

Image denoising is crucial for enhancing image quality, improving visual effects, and boosting the accuracy of image analysis and recognition. Most of the current image denoising methods perform superior on synthetic noise images, but their performance is limited on real-world noisy images since the types and distributions of real noise are often uncertain. To address this challenge, a multi-scale information fusion generative adversarial network method is proposed in this paper. Specifically, In this method, the generator is an end-to-end denoising network that consists of a novel encoder–decoder network branch and an improved residual network branch. The encoder–decoder branch extracts rich detailed and contextual information from images at different scales and utilizes a feature fusion method to aggregate multi-scale information, enhancing the feature representation performance of the network. The residual network further compensates for the compressed and lost information in the encoder stage. Additionally, to effectively aid the generator in accomplishing the denoising task, convolution kernels of various sizes are added to the discriminator to improve its image evaluation ability. Furthermore, the dual denoising loss function is presented to enhance the model’s capability in performing noise removal and image restoration. Experimental results show that the proposed method exhibits superior objective performance and visual quality than some state-of-the-art methods on three real-world datasets.