The practice of using cache to accelerate image super-resolution algorithm in Golang.

With the continuous advancement of technology, high-definition images have become the daily standard used by people. In order to meet this requirement, image super-resolution algorithms emerged, which can convert low-resolution images into high-resolution images through algorithmic operations. However, since this algorithm consumes a large amount of computing resources, it runs slowly. This article will introduce the practice of using cache to accelerate image super-resolution algorithm, implemented by using Golang.

1. Introduction to super-resolution algorithm

The task of super-resolution (SR) is to reconstruct a high-resolution (HR) image from a low-resolution (LR) observation. The HR image has more pixels than the LR image, that is, the high-resolution image has higher details and more comprehensive information. To achieve this goal, the SR algorithm first processes the image through some specific algorithms and then generates the missing details in the HR image.

2. The problem of the running speed of the SR algorithm

For the SR algorithm, the biggest problem is the slow speed, because it has to calculate a lot of content. Not only does it require numerous operations, but it also consumes a large amount of computing resources. When designing the SR algorithm, calculation speed must be taken into consideration and corresponding optimization methods must be adopted, such as using cache.

3. The practice of using cache to accelerate SR algorithm in Golang

Golang is a programming language with excellent performance and easy to write. The language has many excellent libraries and frameworks. Here, we will introduce how to use caching in Golang to speed up the SR algorithm.

1. Prepare resources

First, prepare some resources, including algorithm implementation code, test data and cache library code, etc. We use go module to manage dependencies.

module super-resolution

go 1.12

require (
    github.com/disintegration/imaging v1.5.1
    github.com/gobuffalo/packr v1.27.1
)

Among them, disintegration/imaging is a tool library for image processing; gobuffalo/packr is a resource packaging library.

2. Caching images

Caching is an optimization solution that stores data images in faster storage media. It can greatly accelerate the running speed of the SR algorithm. Here we use memory cache. When the same data is in the cache, we fetch it directly from memory instead of recalculating.

type Cache struct {
  items map[string]interface{}
  m     sync.RWMutex
}

func (c *Cache) Set(key string, value interface{}) {
  c.m.Lock()
  defer c.m.Unlock()
  c.items[key] = value
}

func (c *Cache) Get(key string) (interface{}, bool) {
  c.m.RLock()
  defer c.m.RUnlock()
  value, exists := c.items[key]
  return value, exists
}

func (c *Cache) Delete(key string) {
  c.m.Lock()
  defer c.m.Unlock()
  delete(c.items, key)
}

func (c *Cache) Clear() {
  c.m.Lock()
  defer c.m.Unlock()
  c.items = map[string]interface{}{}
}

3. Implementing the SR algorithm

With the cache, we can implement the SR algorithm and then cache the results.

After using cache, we can greatly reduce the calculation time and improve the running speed of the SR algorithm.

func Upsample(imagePath string, scale float64) image.Image {

    if cache, exist := gcache.Get(imagePath); exist {
        if img, ok := cache.(image.Image); ok {
            return img
        }
    }

    // 缓存没找到，重新读入文件
    img, err := imaging.Open(imagePath)
    if err != nil {
        panic(err)
    }

    // 实现超分辨率算法
    newImg := doSuperResolution(img, scale)

    // 缓存结果
    gcache.Set(imagePath, newImg)
    return newImg
}

4. Packaging resources

We use gobuffalo/packr to package the cached data into the program to facilitate reading when the program is running.

func PackData() {
    bs, _ := gcache.MarshalJSON()
    data := string(bs)

    if err := packr.WriteToFile("super-resolution/data/config.json", data); err != nil {
        panic(err)
    }
}

5. Run the cache in the program

When the program is running, we read the packaged data and load it into the program, and then the program can use the cache directly.

// 读取缓存数据
func initCache() {
    content, err := packr.MustFindString("data/config.json")
    if err != nil {
        panic(err)
    }

    var data map[string]interface{}
    if err := json.Unmarshal([]byte(content), &data); err != nil {
        panic(err)
    }

    // 将缓存数据加载到程序中
    for k, v := range data {
        gcache.Set(k, v)
    }
}

Note: The cache function should be considered based on the actual application scenario. If there is a lot of cached data, it will cause the program to occupy too much memory space. Therefore, when using cache acceleration algorithms, we need to carefully weigh the performance and memory overhead of the program.

4. Summary

Golang provides good concurrency support and multi-thread processing capabilities, which makes it an ideal choice for implementing high-performance algorithms, and cache plays a role in accelerating the speed of SR algorithms. important role. In this article, we introduce how to use cache to optimize the SR algorithm and its practical application in Golang. Through such an optimization algorithm, we can greatly improve the operating performance of the SR algorithm.

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