The invention relates to a vision significance detection method based on Weber's law and center-periphery
hypothesis. The conventional method is low in resolution, incomplete in extracted object outline and high in computational complexity. The method comprises the following steps of: extracting color component graphs of original images in a CIELAB space by adopting a
color transformation method; calculating horizontal gradient difference excitation values and
vertical gradient difference excitation values of pixel points in the 1 color component graph, the color component graph and the b color component graph according to the Weber's law; calculating the difference excitation value of a random
gradient direction according to the horizontal gradient difference excitation values and the
vertical gradient difference excitation values, and counting a difference excitation value
histogram; and finally, establishing local significance excitation vectors of the pixel points to obtain local significance judgment values and overall significance excitation values, and calculating the significance judgment values according to the local significance judgment values and the overall significance excitation values. By the method, vision significance graphs with the same resolution as the input images can be acquired, and stronger response in a significance region can be realized.