Abstract: An improved ghost-artifact detection and removal method for high-dynamic range (HDR) image creation and related apparatus are described. A binary ghost map is first generated for each image of the multiple input images by a ghost-artifact detection process, where one of the binary values indicate ghost pixels and the other indicates non-ghost pixels. Each binary ghost map is smoothed to generate a continuous-tone ghost map, by changing the pixel value of each non-ghost pixel of the binary map to a ghost value between the two binary values. The ghost value is calculated using a monotonous function of the distance between the non-ghost pixel of the binary map and the nearest ghost pixel. Ghost pixels in the binary ghost map are kept as fully ghost pixel in the continuous-tone ghost map. This method helps to reduce visibility of artifacts at ghost boundaries without losing small detected ghost regions.

Abstract: In high dynamic range (HDR) image creation, a ghost artifact detection method divides the images (brackets) into multiple tiles, and selects one bracket for each tile as the local reference bracket. The local reference brackets are selected via optimization of an objective function which includes both a component that measures exposure quality of individual tiles and a component that measures correlation between neighboring tiles. The minimization can be realized by constructing a graph for the objective function and calculating a minimum cut of the graph using a graph cut algorithm. Graph examples for three and four image sets are given. Ghost artifact detection is then performed on a tile-by-tile basis by using the local reference bracket for each tile. Ghost maps are generated this way and used for HDR image creation. This method minimizes artifacts due to inconsistencies in local reference bracket selection in areas involved in ghost-inducing objects.

Abstract: In high dynamic range (HDR) image creation, a ghost artifact detection method divides the images (brackets) into multiple tiles, and selects one bracket for each tile as the local reference bracket. The local reference brackets are selected via optimization of an objective function which includes both a component that measures exposure quality of individual tiles and a component that measures correlation between neighboring tiles. The minimization can be realized by constructing a graph for the objective function and calculating a minimum cut of the graph using a graph cut algorithm. Graph examples for three and four image sets are given. Ghost artifact detection is then performed on a tile-by-tile basis by using the local reference bracket for each tile. Ghost maps are generated this way and used for HDR image creation. This method minimizes artifacts due to inconsistencies in local reference bracket selection in areas involved in ghost-inducing objects.

Abstract: An improved ghost-artifact detection and removal method for high-dynamic range (HDR) image creation and related apparatus are described. A binary ghost map is first generated for each image of the multiple input images by a ghost-artifact detection process, where one of the binary values indicate ghost pixels and the other indicates non-ghost pixels. Each binary ghost map is smoothed to generate a continuous-tone ghost map, by changing the pixel value of each non-ghost pixel of the binary map to a ghost value between the two binary values. The ghost value is calculated using a monotonous function of the distance between the non-ghost pixel of the binary map and the nearest ghost pixel. Ghost pixels in the binary ghost map are kept as fully ghost pixel in the continuous-tone ghost map. This method helps to reduce visibility of artifacts at ghost boundaries without losing small detected ghost regions.

Abstract: A method for detecting ghost artifact in multiple images during high dynamic range (HDR image creation. For each pair of images, multiple individual consistency maps are generated by calculating a consistency function using moving windows of different sizes. The individual consistency maps are combined into a combined consistency map and then binarized. When combining the multiple individual consistency maps, they are first binarized using predetermined threshold values that are window-size-specific. The threshold values are developed beforehand using training images and machine leaning. The final binarized consistency map indicates whether the pair of images are consistent with each other at each pixel location. Then, a ghost-weight map is generated for each image based on the multiple final consistency maps. The ghost-weight map indicates the likelihood of each image pixel being ghost-inducing. The HDR image is generated using the set of images and the corresponding ghost-weight maps.

Abstract: A method for detecting ghost artifact in multiple images during high dynamic range (HDR image creation. For each pair of images, multiple individual consistency maps are generated by calculating a consistency function using moving windows of different sizes. The individual consistency maps are combined into a combined consistency map and then binarized. When combining the multiple individual consistency maps, they are first binarized using predetermined threshold values that are window-size-specific. The threshold values are developed beforehand using training images and machine leaning. The final binarized consistency map indicates whether the pair of images are consistent with each other at each pixel location. Then, a ghost-weight map is generated for each image based on the multiple final consistency maps. The ghost-weight map indicates the likelihood of each image pixel being ghost-inducing. The HDR image is generated using the set of images and the corresponding ghost-weight maps.

Abstract: A method for selecting an optimum subset of images from a larger set of images for optimal HDR image creation. For each of the set of images, an exposure quality map is computed by assigning, to each pixel of the image, an exposure quality value based on the pixel brightness value of that pixel. A suitable exposure quality function, typically reflecting the properties of the imaging device, is used for this purpose. Then, for each possible subset of images, the exposure quality maps for the images in the subset are combined using a max operation to generate a combined exposure quality map for the subset. An average pixel value of the combined exposure quality map of the subset is then calculated and serves as a quality score. The subset of images processing the highest quality score are used to generate the HDR image.

Abstract: A method for selecting an optimum subset of images from a larger set of images for optimal HDR image creation. For each of the set of images, an exposure quality map is computed by assigning, to each pixel of the image, an exposure quality value based on the pixel brightness value of that pixel. A suitable exposure quality function, typically reflecting the properties of the imaging device, is used for this purpose. Then, for each possible subset of images, the exposure quality maps for the images in the subset are combined using a max operation to generate a combined exposure quality map for the subset. An average pixel value of the combined exposure quality map of the subset is then calculated and serves as a quality score. The subset of images processing the highest quality score are used to generate the HDR image.