The invention discloses a reference-free spliced image quality evaluation method and system, and belongs to the technical field of image processing. The evaluation method comprises the following steps: firstly, calculating an adaptive threshold, and judging the detail complexity of a spliced image; then, determining the number of pixel points in the neighborhood of each pixel point according to the detail complexity of the spliced image, and acquiring a CLBP window gradient difference value of each pixel point through calculation; and then, acquiring the average CLBP window gradient differencevalue of the spliced image through calculation, so that the quality of the spliced image can be evaluated. According to the spliced image quality evaluation method provided by the invention, the quality of the spliced image is objectively evaluated by utilizing the average CLBP window gradient difference value (MCLBP _ M value) of the spliced image, the operation is simple, and an evaluation effect consistent with a subjective evaluation result can be achieved without reference images.

Reference-free spliced image quality evaluation method and system

The invention relates to a noise detection method based on self-defined features of a single image, and the method comprises the steps: collecting a transformer substation image, and calculating an image x-direction gradient graph and an image y-direction gradient graph; traversing the to-be-detected image, and calculating a gradient direction angle of each pixel point according to the gradient map; giving a direction distribution number, calculating a direction distribution value of each pixel point in each direction according to the gradient value and the direction angle, and calculating anintegral graph so as to realize self-defined feature extraction; carrying out statistical analysis on the image gradient information and the self-defined features according to a self-defined feature extraction result, and obtaining an average gradient value and a main direction distribution value under given conditions; calculating an image noise degree, giving a noise degree threshold value, if the noise degree is greater than the given threshold value, considering that the image belongs to a noise image, and outputting the noise degree, otherwise, considering that the image belongs to a normal image, thereby realizing image noise detection. The method solves the problem of noise degree detection of a single image, and has important practical application value.

Chen Tai

Papers

Noise detection method based on self-defined features of a single image