Image denoising with multiple layer block matching and 3D filtering

Abstract

Block Matching and 3-D Filtering (BM3D) algorithm is currently considered as one of the most successful denoising algorithms. Despite its excellent results, BM3D still has room for improvements. Image details and sharp edges, such as text in document images are challenging, as they usually do not produce sparse representations under the linear transformations. Various artifacts such as ringing and blurring can be introduced as a result.This paper proposes a Multiple Layer BM3D (MLBM3D) denoising algorithm. The basic idea is to decompose image patches that contain high contrast details into multiple layers, and each layer is then collaboratively filtered separately.The algorithm contains a Basic Estimation step and a Final Estimation step. The first (Basic Estimation) step is identical to the one in BM3D. In the second (Final Estimation) step, image groups are determined to be single-layer or multilayer. A single-layer group is filtered in a same manner as in BM3D. For a multi-layer group, each image patch within the group is decomposed with the three-layer model. All the top layers in the group are stacked and collaboratively filtered. So are the bottom layers. The filtered top and bottom layers are re-assembled to form the estimation of the blocks. The final estimation of the image is obtained by aggregating estimations of all blocks, including both single-layer and multi-layer ones. The proposed algorithm shows excellent results, particularly for the images containing high contrast edges.

SID-TGAN: A Transformer-Based Generative Adversarial Network for Sonar Image Despeckling

“…Han et al [18] proposed an improved BM3D sonar image denoising algorithm, which adjusts parameters based on the noise characteristics of sonar images and incorporates Gaussian filtering and grayscale correction before the basic estimation to eliminate speckle noise. Fan et al [5] considered converting multiplicative speckle noise into additive Gaussian noise and performing speckle denoising on sonar images through adaptive BM3D. Chen et al [8] proposed a speckle noise reduction method for side-scan sonar images based on adaptive BM3D.…”

“…The interest in sonar image despeckling has been intense since the early 1980s. From then on, many effective filtering-based methods [3][4][5][6][7][8][9] were proposed for sonar image despeckling. However, filtering-based methods suffer from two fundamental drawbacks.…”