Multiscale Sparsifying Transform Learning for Image Denoising
This work provides an incremental improvement for image denoising by enhancing efficiency and performance in computational imaging applications.
The paper tackled the problem of image denoising by addressing the suboptimal results of single-scale data-driven sparse methods, proposing multiscale extensions that reduce runtime without denoising detail subbands, and achieved good trade-offs between performance and complexity in experiments.
The data-driven sparse methods such as synthesis dictionary learning (e.g., K-SVD) and sparsifying transform learning have been proven effective in image denoising. However, they are intrinsically single-scale which can lead to suboptimal results. We propose two methods developed based on wavelet subbands mixing to efficiently combine the merits of both single and multiscale methods. We show that an efficient multiscale method can be devised without the need for denoising detail subbands which substantially reduces the runtime. The proposed methods are initially derived within the framework of sparsifying transform learning denoising, and then, they are generalized to propose our multiscale extensions for the well-known K-SVD and SAIST image denoising methods. We analyze and assess the studied methods thoroughly and compare them with the well-known and state-of-the-art methods. The experiments show that our methods are able to offer good trade-offs between performance and complexity.