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公开(公告)号:US20250111486A1
公开(公告)日:2025-04-03
申请号:US18961793
申请日:2024-11-27
Applicant: APPLE INC.
Inventor: Ching E. HO , D. Amnon Silverstein
IPC: G06T5/70 , G06T3/4046 , G06T7/10
Abstract: Some embodiments relate to sharpening segments of an image differently based on content in the image. Content based sharpening is performed by a content image processing circuit that receives luminance values of an image and a content map. The content map identifies categories of content in segments of the image. Based on one or more of the identified categories of content, the circuit determines a content factor associated with a pixel. The content factor may also be based on a texture and/or chroma values. A texture value indicates a likelihood of a category of content and is based on detected edges in the image. A chroma value indicates a likelihood of a category of content and is based on color information of the image. The circuit receives the content factor and applies it to a version of the luminance value of the pixel to generate a sharpened version of the luminance value.
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公开(公告)号:US20250124555A1
公开(公告)日:2025-04-17
申请号:US18900476
申请日:2024-09-27
Applicant: Apple Inc.
Inventor: Ching E. HO , Jackson K. ROLAND , Nicolas P. BONNIER
Abstract: Disclosed are various techniques for generating image specific global tone curves and residual gain maps based on multiple images. A gain map is generated based on a comparison of a first image to a second image. A gain map scatter plot is constructed based on the gain map values and channel values of either the first image or the second image. An image specific global tone curve is determined by curve fitting to the gain map scatter plot. A residual gain map is generated based on a comparison of the gain map to a reconstructed gain map derived from the image specific global tone curve. An approximation of the second image can be constructed using the image specific global tone curve and the first image. A replica of the second image can be constructed using the image specific global tone curve, the residual gain map, and the first image.
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