Golang image processing: how to repair pictures and texture synthesis

Golang Image Processing: How to Repair Pictures and Texture Synthesis

Introduction: Image processing is one of the important fields in modern computer vision and computer graphics. In image processing, repairing corrupted images and synthesizing textures is one of the very common and interesting tasks. This article will introduce how to use Golang for image restoration and texture synthesis, and provide code examples.

1. Image Repair

In image processing, image repair is a technology that improves image quality by repairing damaged images or removing noise from images. In Golang, we can use some image processing libraries to implement image repair algorithms.

1.1 Image restoration based on domain transformation

Domain transformation is an image processing technology that achieves image restoration by matching and replacing a part of the image with the surrounding area. In Golang, we can use the go-image library to implement this algorithm.

The following is a sample code that uses the domain transformation algorithm to repair an image:

import ( "github.com/disintegration/gift" "github.com/vitali-fedulov/images" ) func main() { // 加载原始图像 img, _ := images.Open("input.jpg") // 对原始图像应用高斯模糊以去除噪声 blur := gift.New(gift.GaussianBlur(2)) imgBlur := img.Clone().Bounds(img.Bounds()) blur.Draw(imgBlur, img) // 对修复之后的图像应用领域变换算法 patchSize := 5 blend := gift.New(gift.Blender(nil, gift.Copy)) dt := images.DenoiseTransform{ PatchRadius: patchSize, SearchWindowRadius: 2 * patchSize, } repairedImg := img.Clone().Bounds(img.Bounds()) dt.Draw(repairedImg, imgBlur) // 将修复之后的图像保存为新的文件 images.Save(repairedImg, "output.jpg") }

In the above code, we first load the original image and use Gaussian blur to remove the noise in the image. Then, we use the domain transformation algorithm to repair the repaired image, and save the repaired image as a new file.

1.2 Image restoration based on deep learning

Deep learning is one of the very hot fields in recent years, and it can achieve amazing results in many image processing tasks. In image repair, we can also use deep learning to perform image repair.

In Golang, we can use the go-deepcv library to implement deep learning-based image repair algorithms. Here is a sample code that uses this library to implement image inpainting:

import ( "github.com/LdDl/gocv" "github.com/LdDl/gocv/opencv" ) func main() { // 加载原始图像 img := gocv.IMRead("input.jpg", opencv.IMReadUnchanged) // 创建神经网络模型 model := gocv.TexturedInpainting() // 对图像进行修复 repairedImg := gocv.NewMat() model.Inpaint(img, repairedImg) // 将修复之后的图像保存为新的文件 gocv.IMWrite("output.jpg", repairedImg) }

In the above code, we first load the original image and create a neural network model. We then use the model to repair the image and save the repaired image as a new file.

2. Texture synthesis

Texture synthesis is an image processing technology that can synthesize different textures into a new texture image. In Golang, we can use the go-image library to implement texture synthesis algorithms.

The following is a sample code for texture synthesis using the texture synthesis algorithm:

import ( "github.com/disintegration/gift" "github.com/vitali-fedulov/images" ) func main() { // 加载纹理图像和目标图像 texture, _ := images.Open("texture.jpg") target, _ := images.Open("target.jpg") // 将纹理图像调整到和目标图像一样的尺寸 resizedTexture := images.Resize(texture, target.Bounds().Dx(), target.Bounds().Dy()) // 将纹理图像和目标图像进行融合 blend := gift.New(gift.BlendWithMode(resizedTexture, gift.Normal, 1.0)) result := target.Clone().Bounds(target.Bounds()) blend.Draw(result, target) // 保存合成后的图像为新的文件 images.Save(result, "output.jpg") }

In the above code, we first load the texture image and the target image, and adjust the texture image to and The same size as the target image. Then, we use a fusion algorithm to synthesize the texture image and the target image, and save the synthesized image as a new file.

Conclusion:

This article introduces how to use Golang for image repair and texture synthesis, and provides corresponding code examples. By learning and applying these techniques, we can achieve richer and more interesting effects in image processing. Hope this article is helpful to you.

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