In this paper, we propose a novel locally statistical variational active
contour model based on I-divergence-TV denoising model, which hybrides geodesic
active contour (GAC) model with active contours without edges (ACWE) model, and
can be used to segment images corrupted by multiplicative gamma noise. By
adding a diffusion term into the level set evolution (LSE) equation of the
proposed model, we construct a reaction-diffusion (RD) equation, which can
gradually regularize the level set function (LSF) to be piecewise constant in
each segment domain and gain the stable solution. We further transform the
proposed model into classic ROF model by adding a proximity term. Inspired by a
fast denoising algorithm proposed by Jia-Zhao recently, we propose two fast
fixed point algorithms to solve SAR image segmentation question. Experimental
results for real SAR images show that the proposed image segmentation model can
efficiently stop the contours at weak or blurred edges, and can automatically
detect the exterior and interior boundaries of images with multiplicative gamma
noise. The proposed FPRD1/FPRD2 models are about 1/2 (or less than) of the time
required for the SBRD model based on the Split Bregman technique.

本文提出了一种基于 I - 散度 - TV 去噪模型的新型局部统计变分主动轮廓模型，可用于分割被乘性伽马噪声损坏的图像，并通过向所提出模型的水平集演化（LSE）方程添加扩散项来构建反应扩散（RD）方程，进而将所提出模型转化为经典 ROF 模型。

一种用于 SAR 图像分割的局部统计主动轮廓模型可通过降噪算法求解

A locally statistical active contour model for SAR image segmentation  can be solved by denoising algorithms

In this paper, we propose a novel variational active contour model based on
I-divergence-TV model to segment Synthetic aperture radar (SAR) images with
multiplicative gamma noise, which hybrides edge-based model with region-based
model. The proposed model can efficiently stop the contours at weak or blurred
edges, and can automatically detect the exterior and interior boundaries of
images. We incorporate the global convex segmentation method and split Bregman
technique into the proposed model, and propose a fast fixed point algorithm to
solve the global convex segmentation question[25]. Experimental results for
synthetic images and real SAR images show that the proposed fast fixed point
algorithm is robust and efficient compared with the state-of-the-art approach.

该论文提出了一种基于 I-divergence-TV 模型的新型变分主动轮廓模型，用于分割具有乘性伽马噪声的合成孔径雷达（SAR）图像，将基于边缘的模型与基于区域的模型相结合。所提出的模型能够在弱或模糊边缘处有效停止轮廓，并能自动检测图像的外部和内部边界。将全局凸分割方法和分裂布雷格曼技术融入所提出的模型，并提出了一种快速固定点算法来解决全局凸分割问题 [25]。合成图像和实际 SAR 图像的实验结果表明，与现有技术相比，所提出的快速固定点算法具有鲁棒性和高效性。

基于 I - 散度 - TV 模型的 SAR 图像分割算法

SAR image segmentation algorithms based on I-divergence-TV model

Generative diffusion processes are an emerging and effective tool for image
and speech generation. In the existing methods, the underlying noise
distribution of the diffusion process is Gaussian noise. However, fitting
distributions with more degrees of freedom could improve the performance of
such generative models. In this work, we investigate other types of noise
distribution for the diffusion process. Specifically, we introduce the
Denoising Diffusion Gamma Model (DDGM) and show that noise from Gamma
distribution provides improved results for image and speech generation. Our
approach preserves the ability to efficiently sample state in the training
diffusion process while using Gamma noise.

本文提出了一种基于 Gamma 噪声的去噪扩散 Gamma 模型（DDGM），并通过图像和语音生成任务验证了其性能优于传统的基于高斯噪声的扩散模型。