Adjustment Techniques For Uniformity Film

The technique of adjusting the uniform light sheet mainly involves the homogenization of lighting in image processing to reduce the problem of image quality degradation caused by poor lighting conditions. The following are several common techniques for adjusting the uniformity plate:

Brightness adjustment of local areas:
Local area brightness adjustment mainly targets the uneven exposure phenomenon that occurs locally in the image. This usually involves local contrast enhancement techniques, such as local histogram equalization. By independently performing histogram equalization on each local region in the image, the contrast of each region can be enhanced while maintaining the global contrast of the entire image as unchanged as possible.

Global brightness reallocation:
The redistribution of global brightness focuses on adjusting the brightness of the entire image. This method can achieve uniformity of image brightness by changing the shape of the histogram without altering the overall brightness of the image.
Multi scale processing technology:

By analyzing and processing images at different scales, it is possible to effectively adjust brightness while preserving image details. This method combines the advantages of local processing and global processing, and can better adapt to complex lighting conditions.

Using professional software:
In practical operation, image equalization processing is usually implemented using software. For example, some image processing software such as the shadow/highlight tool in Photoshop is a more intuitive method of image smoothing. In addition, there are some professional medical imaging processing software, such as ImageJ, which provides rich image equalization functions for users to perform fine processing on medical images.

Technologies based on deep learning:
In recent years, with the rapid development of deep learning technology, breakthroughs have been made in image smoothing technology based on deep learning. Deep learning models, especially convolutional neural networks (CNNs), can automatically discover and learn the best strategy for image smoothing under different lighting conditions by learning a large amount of labeled image data, thus achieving better processing results than traditional algorithms in some specific application scenarios.

Mask technology:
In image processing, masking technology is widely used for local processing, especially in the process of local brightness adjustment. Masking can protect certain areas from the influence of adjustment and only adjust the brightness of specific areas, thereby controlling the uniform lighting effect more finely.