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Aiming at multi-focus image fusion, the complementary information at each scale is fused in a targeted manner using a multi-scale fusion method. First, the source image is decomposed into a background layer and a detail layer using the L0 gradient minimization smoothing method, and then a fractional-order edge detection mask is used to preserve the edge information in the background layer, and a fractional-order gradient energy operator is introduced in the detail layer for weight assignment. The experimental results show that the proposed fractional-order gradient energy operator is more sensitive than the integer-order gradient energy clarity operator, which is consistent with the human visual perception. The fusion method proposed effectively avoids artifacts and block effects in the fused images, and retains the source image information more fully. [ABSTRACT FROM AUTHOR]

Ｉｎ ｏｒｄｅｒ ｔｏ ａｖｏｉｄ ｔｈｅ ｂｌｕｒｒｉｎｇ ｏｆ ｉｍａｇｅ ｂｙ ｃｌａｓｓｉｃａｌ ｍｅｄｉａｎ ｆｉｌｔｅｒ, ａ ｎｏｉｓｅ ｄｅｔｅｃｔｉｏｎ ｍｏｄｅｌ ｗａｓ ｅｓｔａｂｌｉｓｈｅｄ. Ａ ｓｗｉｔｃｈｉｎｇ ｆｉｌｔｅｒ ｗａｓ ｄｅｓｉｇｎｅｄ ｂｙ ｄｅｔｅｃｔｉｎｇ ｎｏｉｓｅ. Ｗｈｅｎ ｔｈｅ ｄｅｔｅｃｔｉｏｎ ｐｏｉｎｔ ｉｓ ｎｏｉｓｙ, ｔｈｅ ｍｅｄｉａｎ ｆｉｌｔｅｒ ｗａｓ ｕｓｅｄ ｆｏｒ ｄｅｎｏｉｓｉｎｇ. Ｗｈｅｎ ｔｈｅ ｄｅｔｅｃｔｉｏｎ ｐｏｉｎｔ ｉｓ ｎｏｎ-ｎｏｉｓｙ ｐｏｉｎｔ, ｔｈｅ ｆｒａｃｔｉｏｎａｌ ｄｉｆｆｅｒｅｎｔｉａｌ ｆｉｌｔｅｒ ｗａｓ ｕｓｅｄ ｔｏ ｅｎｈａｎｃｅ ｔｈｅ ｉｍａｇｅ. Ｔｈｅ ｐｒｏｐｏｓｅｄ ａｌｇｏｒｉｔｈｍ ｃａｎ ｎｏｔ ｏｎｌｙ ｅｆｆｅｃｔｉｖｅｌｙ ｒｅｍｏｖｅ ｔｈｅ ｓａｌｔ ａｎｄ ｐｅｐｐｅｒ ｎｏｉｓｅ ｉｎ ｔｈｅ ｉｍａｇｅ, ｂｕｔ ａｌｓｏ ｅｎｈａｎｃｅ ｔｈｅ ｉｍａｇｅ, ｓｏ ｔｈａｔ ｔｈｅ ｉｍａｇｅ ｒｅｔａｉｎｓ ｔｈｅ ｄｅｔａｉｌｓ ｉｎｔａｃｔ ｗｈｅｎ ｔｈｅ ｅｄｇｅ ｉｓ ｐｒｏｔｒｕｄｉｎｇ. Ｓｅｌｅｃｔｉｎｇ “Ｌｅｎａ” ａｎｄ ｏｔｈｅｒ ｃｌａｓｓｉｃ ｉｍａｇｅｓ ｆｏｒ ｍｕｌｔｉｐｌｅ ｅｘｐｅｒｉｍｅｎｔｓ ａｎｄ ａｎａｌｙｓｉｓ, ｔｈｅ ｒｅｓｕｌｔｓ ｓｈｏｗｅｄ ｔｈｅ ｅｆｆｅｃｔｉｖｅｎｅｓｓ ｏｆ ｔｈｅ ｐｒｏｐｏｓｅｄ ａｌｇｏｒｉｔｈｍ ｉｎ ｉｍａｇｅ ｄｅｎｏｉｓｉｎｇ ａｎｄ ｅｎｈａｎｃｅｍｅｎｔ. [ABSTRACT FROM AUTHOR]