Python 如何提高图像质量？

我正在做一个读身份证的OCR。在使用YOLO获得感兴趣的区域后，我将该裁剪区域交给Tesseract阅读。由于这些裁剪的图像非常小且模糊，Tesseract无法读取它们。当它能读懂它们时，就会给出错误的预测。我认为，通过提高裁剪图像的图像质量，可以解决这些问题

剪切的图像之一：

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我的问题是，我将如何改进这些图像？

一种方法是将图像转换为灰度，然后使用阈值与每个像素进行比较，以决定它是黑色还是白色。是可用于此类型处理的库：

from PIL import Image

black = (0,0,0)
white = (255,255,255)
threshold = (160,160,160)

# Open input image in grayscale mode and get its pixels.
img = Image.open("image.jpg").convert("LA")
pixels = img.getdata()

newPixels = []

# Compare each pixel 
for pixel in pixels:
    if pixel < threshold:
        newPixels.append(black)
    else:
        newPixels.append(white)

# Create and save new image.
newImg = Image.new("RGB",img.size)
newImg.putdata(newPixels)
newImg.save("newImage.jpg")

结果图像：

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@vasilisg的答案。这是一个很好的解决方案。进一步改进的一种方法是使用形态学打开操作去除剩余斑点。但是，这仅适用于小于图像中数字线宽的点。另一个选项是使用openCV连接组件模块删除小于N像素的“孤岛”。例如，您可以按以下方式执行此操作：

# External libraries used for
# Image IO
from PIL import Image

# Morphological filtering
from skimage.morphology import opening
from skimage.morphology import disk

# Data handling
import numpy as np

# Connected component filtering
import cv2

black = 0
white = 255
threshold = 160

# Open input image in grayscale mode and get its pixels.
img = Image.open("image.jpg").convert("LA")
pixels = np.array(img)[:,:,0]

# Remove pixels above threshold
pixels[pixels > threshold] = white
pixels[pixels < threshold] = black


# Morphological opening
blobSize = 1 # Select the maximum radius of the blobs you would like to remove
structureElement = disk(blobSize)  # you can define different shapes, here we take a disk shape
# We need to invert the image such that black is background and white foreground to perform the opening
pixels = np.invert(opening(np.invert(pixels), structureElement))


# Create and save new image.
newImg = Image.fromarray(pixels).convert('RGB')
newImg.save("newImage1.PNG")

# Find the connected components (black objects in your image)
# Because the function searches for white connected components on a black background, we need to invert the image
nb_components, output, stats, centroids = cv2.connectedComponentsWithStats(np.invert(pixels), connectivity=8)

# For every connected component in your image, you can obtain the number of pixels from the stats variable in the last
# column. We remove the first entry from sizes, because this is the entry of the background connected component
sizes = stats[1:,-1]
nb_components -= 1

# Define the minimum size (number of pixels) a component should consist of
minimum_size = 100

# Create a new image
newPixels = np.ones(pixels.shape)*255

# Iterate over all components in the image, only keep the components larger than minimum size
for i in range(1, nb_components):
    if sizes[i] > minimum_size:
        newPixels[output == i+1] = 0

# Create and save new image.
newImg = Image.fromarray(newPixels).convert('RGB')
newImg.save("newImage2.PNG")

在本例中，我执行了打开和连接组件方法，但是如果使用连接组件方法，通常可以忽略打开操作

结果如下所示：

阈值设定和打开后：

阈值化、打开和连接组件过滤后：

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您必须使用哪些工具来提高图像质量，这些工具是否有任何限制？我认为没有限制