Edge Detection Algorithm for Unevenly Illuminated Images Based on Parameterized Logarithmic Image Processing Model

Due to the imaging characteristics of images under low illumination and uneven scene light conditions, ordinary edge detection methods cannot effectively detect complete and clear edge images. This paper proposes a new edge detection algorithm based on the advantages of the parameterized logarithmic image processing (PLIP) model, which is sensitive to low-brightness images and the processing effect is close to the results of human visual observation. First, we use the PLIP model theory to derive a new gradient operator, and then analyze the shortcomings of the traditional Canny algorithm to detect the edge process and improve it, and replace the gradient in the traditional Canny algorithm with the new gradient derived from the Canny algorithm. The best edge detection standard extracts the edge of the image. Finally, the edge detection is compared and verified by the low illuminance image obtained by the low-light experimental platform and the image under the uneven scene light environment. The experimental results show that the linear connection degree of the edge detection of the new algorithm is about 10%, 30%, and 4% higher than that of the traditional Canny algorithm, Sobel algorithm, and original LIP algorithm, respectively, and its detection effect is better.