An Improved Combination of Image Denoisers Using Spatial Local Fusion Strategy

Abstract

Image denoising is a well-researched problem in the image processing field. Numerous image denoising algorithms have been proposed in the past. Although researchers have continually focused on improving the denoising algorithm performance regarding denoising effect and outstanding results have been achieved, the improvement amplitude of a single denoising algorithm has decreased over time. Recently, the consensus neural network (CsNet), which combines multiple image denoisers to produce an overall better result compared to single algorithms, was proposed. However, the denoising process of CsNet is time-consuming owing to its MSE optimal weight setting and iterative boosting stages. Therefore, we propose an improved combination of nonlocally centralized sparse representation (NCSR) with a fast and flexible denoising convolutional neural network (FFDNet) using a spatial local fusion strategy (ICID). ICID uses a structural-based patch to decompose their denoised images into the strength, structure, and mean intensity components. Thereafter, an image patch is reconstructed and placed back into the fused image after fusing the three components separately. Experimental results verified that our algorithm is superior to CsNet, and it is faster. The combination of NCSR and FFDNet can harmonize the complementary denoising capabilities of different denoising algorithms. NCSR can preserve as many details as possible in natural images with numerous repeated structures, whereas FFDNet can achieve state-of-the-art results with a sufficiently large training set of images. Moreover, ICID uses the structural-based method, which considers more local details and preserves more textures, resulting in superior performance.