# An Unsupervised Weight Map Generative Network for Pixel-Level Combination of Image Denoisers
###### Abstract  
Image denoising is a classic but still important issue in image processing as the denoising effect has a significant impact on subsequent image processing results, such as target recognition and edge detection. In the past few decades, various denoising methods have been proposed, such as model-based and learning-based methods, and they have achieved promising results. However, no stand-alone method consistently outperforms the others in different complex imaging situations. Based on the complementary strengths of model-based and learning-based methods, in this study, we design a pixel-level image combination strategy to leverage their respective advantages for the denoised images (referred to as initial denoised images) generated by individual denoisers. The key to this combination strategy is to generate a corresponding weight map of the same size for each initial denoised image. To this end, we introduce an unsupervised weight map generative network that adjusts its parameters to generate a weight map for each initial denoised image under the guidance of our designed loss function. Using the weight maps, we are able to fully utilize the internal and external information of various denoising methods at a finer granularity, ensuring that the final combined image is close to the optimal. To the best of our knowledge, our enhancement method of combining denoised images at the pixel level is the first proposed in the image combination field. Extensive experiments demonstrate that the proposed method shows superior performance, both quantitatively and visually, and stronger generalization. Specifically, in comparison with the stand-alone denoising methods FFDNet and BM3D, our method improves the average peak signal-to-noise ratio (PSNR) by 0.18 dB to 0.83 dB on two benchmarking datasets crossing different noise levels. Its denoising effect is also greater than other competitive stand-alone methods and combination methods, and has surpassed the denoising effect of the second-best method by 0.03 dB to 1.42 dB. It should be noted that since our image combination strategy is generic, the proposed combined strategy can not only be used for image denoising but can also be extended to low-light image enhancement, image deblurring or image super-resolution. image denoising; noise reduction effect; image combination; unsupervised learning; pixel-level combination +
Footnote †: journal: Medical Image Processing  
0000-0001-8800-7000]X.X. problem with no unique solution from a mathematical standpoint [6; 7; 8]. Generally, image denoising can be modeled mathematically as follows:  
\[y=x+n, \tag{1}\]  
where \(y\) is the noisy image, \(x\) is the clean image, and \(n\) is the noise, which is usually modeled using standard deviation \(\sigma\) as additive white Gaussian noise (AWGN) [9]. In the past few decades, a variety of denoising methods have been proposed, which can be categorized into two groups: model-based and learning-based denoising methods [10]. In earlier years, the typical methods were model-based. Earlier model-based methods rely on the statistical analysis of the internal information of the given images. Representative examples of model-based methods are non-local means (NLM) [11] and block matching and 3D filtering (BM3D) [12]. In 2005, [PERSON] et al. first utilized non-local self-similarity in images and proposed NLM. Based on the observation that a natural image patch includes several similar counterparts inside the same image, NLM removes the noise by stacking non-local self-similar patches, which works well with images that have repeating patterns in spatially diverse regions. However, it is computationally impossible to search for similar patches within the whole image; hence, usually, a small vicinity of the patch is explored while looking for optimal matches [13]. Later, inspired by the idea of NLM, [PERSON] et al. proposed BM3D, which included a two-step denoising process. First, the input image was denoised and a basic estimate of image blocks was generated.