Abstract:
An edge parameter computing method for an image, wherein the image includes a plurality of pixels forming a Bayer pattern. The edge parameter computing method comprises: (a) computing an average grey level of at least one specific type pixels in a specific region of the image; (b) computing each grey level difference value between the average grey level and the specific type pixels in the specific region to generate a plurality of grey level difference values; (c) finding a specific pixel with a maximum grey level difference value according to the grey level difference values; and (d) computing a ratio value between the average grey level and the maximum grey level difference value as the edge parameter.
Abstract:
A camera with defective pixel compensation is provided. The camera comprises a register and a compensating unit. The compensating unit receives an image datum and a plurality of adjacent image data relating to the image datum and, according to the value installed in the register, the compensator selects a reference datum from the plurality of adjacent image data. When the image datum is greater than the reference datum by a threshold value, the compensating unit modifies the image datum according to the reference datum.
Abstract:
A camera with defective pixel compensation is provided. The camera comprises a register and a compensating unit. The compensating unit receives an image datum and a plurality of adjacent image data relating to the image datum and, according to the value installed in the register, the compensator selects a reference datum from the plurality of adjacent image data. When the image datum is greater than the reference datum by a threshold value, the compensating unit modifies the image datum according to the reference datum.
Abstract:
An edge parameter computing method for an image, wherein the image includes a plurality of pixels forming a Bayer pattern. The edge parameter computing method comprises: (a) computing an average grey level of at least one specific type pixels in a specific region of the image; (b) computing each grey level difference value between the average grey level and the specific type pixels in the specific region to generate a plurality of grey level difference values; (c) finding a specific pixel with a maximum grey level difference value according to the grey level difference values; and (d) computing a ratio value between the average grey level and the maximum grey level difference value as the edge parameter.