Pinned Repositories
an-improved-NLM-image-denoising-algorithm-based-on-edge-detection
Aiming at the removal of gaussian noise, we systematically analyze the shortage of non-local means image denonising algorithm (NLM), finding it is easy to lose structure information when dealing with the image containing complex edges and textures by NLM algorithm. In order to solve this problem, a non-local means image denoising based on edge detection is proposed in this thesis. The innovation of the proposed algorithm is mainly manifested in the following : (1) An improved Sobel operator with eight directions is proposed to extract a more accurate edge image; (2) To make the neighborhoods with similar structure obtain more weight, not only the Euclidean distance but also the edge image are considered when the similarity of neighborhoods is measured. Many experiments demonstrate that in both subjective and objective evaluation principles the performance of the improved algorithm has a good effect, and the visual effect of the denoised image is good.
EightAlgorithms
八种常用的排序算法：插入排序、冒泡排序、选择排序、希尔排序、快速排序、归并排序、堆排序和LST基数排序的Java和C++代码实现
learning-asm
汇编语言（第2版）-王爽，所有源码和练习
LeetCode-CS
The C# solutions for LeetCode problems.
noise-adaptive-switching-non-local-means
Aiming at the removal of salt-and-pepper noise, a noise adaptive switching non-local means denoising algorithm (NASNLM) is proposed in this program. For noise detection, the pixels of image are divided into the noise and the non-noise points. For filtering, four different filtering techniques are adopted: switching filtering, noise adaptive median filtering, edge-perserving filtering and non-local means filtering. Switching filtering can keep the gray-value of non-noise points unchanged. Noise adaptive median filtering can suppress the high-density salt-and-pepper noise. Edge-preserving filtering can preserve more image edges and details. Non-local means filtering can further improve the ability of noise suppression and detail maintenance. Experiments demonstrate that for removal of the high-density salt-and-pepper noise by NASNLM algorithm, a better denoising effect is obtained than other methods.
gkh178's Repositories
gkh178/noise-adaptive-switching-non-local-means
Aiming at the removal of salt-and-pepper noise, a noise adaptive switching non-local means denoising algorithm (NASNLM) is proposed in this program. For noise detection, the pixels of image are divided into the noise and the non-noise points. For filtering, four different filtering techniques are adopted: switching filtering, noise adaptive median filtering, edge-perserving filtering and non-local means filtering. Switching filtering can keep the gray-value of non-noise points unchanged. Noise adaptive median filtering can suppress the high-density salt-and-pepper noise. Edge-preserving filtering can preserve more image edges and details. Non-local means filtering can further improve the ability of noise suppression and detail maintenance. Experiments demonstrate that for removal of the high-density salt-and-pepper noise by NASNLM algorithm, a better denoising effect is obtained than other methods.
gkh178/an-improved-NLM-image-denoising-algorithm-based-on-edge-detection
Aiming at the removal of gaussian noise, we systematically analyze the shortage of non-local means image denonising algorithm (NLM), finding it is easy to lose structure information when dealing with the image containing complex edges and textures by NLM algorithm. In order to solve this problem, a non-local means image denoising based on edge detection is proposed in this thesis. The innovation of the proposed algorithm is mainly manifested in the following : (1) An improved Sobel operator with eight directions is proposed to extract a more accurate edge image; (2) To make the neighborhoods with similar structure obtain more weight, not only the Euclidean distance but also the edge image are considered when the similarity of neighborhoods is measured. Many experiments demonstrate that in both subjective and objective evaluation principles the performance of the improved algorithm has a good effect, and the visual effect of the denoised image is good.
gkh178/learning-asm
汇编语言（第2版）-王爽，所有源码和练习
gkh178/EightAlgorithms
八种常用的排序算法：插入排序、冒泡排序、选择排序、希尔排序、快速排序、归并排序、堆排序和LST基数排序的Java和C++代码实现
gkh178/LeetCode-CS
The C# solutions for LeetCode problems.