Deep Convolutional Dictionary Learning Denoising Method Based on Distributed Image Patches

Abstract

To address susceptibility to noise interference in Micro-LED displays, a deep convolutional dictionary learning denoising method based on distributed image patches is proposed in this paper. In the preprocessing stage, the entire image is partitioned into locally consistent image patches, and a dictionary is learned based on the non-local self-similar sparse representation of distributed image patches. Subsequently, a convolutional dictionary learning method is employed for global self-similarity matching. Local constraints and global constraints are combined for effective denoising, and the final denoising optimization algorithm is obtained based on the confidence-weighted fusion technique. The experimental results demonstrate that compared with traditional denoising methods, the proposed denoising method effectively restores fine-edge details and contour information in images. Moreover, it exhibits superior performance in terms of PSNR and SSIM. Particularly noteworthy is its performance on the grayscale dataset Set12. When evaluated with Gaussian noise σ=50, it outperforms DCDicL by 3.87 dB in the PSNR and 0.0012 in SSIM.

IFSrNet: Multi-Scale IFS Feature-Guided Registration Network Using Multispectral Image-to-Image Translation

“…This approach is more advantageous for high-level semantic feature extraction. Specifically, this paper adopts the multi-head attention [32] mechanism proposed in Transformer [33], whereby the three modular inputs are passed through a convolutional block to obtain Q, K, and V. Subsequently, the acquired Q, K, and V are employed to execute multi-head attention computation. This attention mechanism enables the generation of a more comprehensive feature representation by leveraging the multi-head attention.…”