An image restoration method includes determining an anchor image based on individual images of a burst image set, executing a feature extraction network based on the burst image set while using anchor information of the anchor image, and generating a restored image based on a feature map corresponding to an output of the feature extraction network.

An apparatus includes a wavefront sensor configured to receive coherent flood illumination that is reflected from a remote object and to estimate wavefront errors associated with the coherent flood illumination. The apparatus also includes a beam director optically coupled to the wavefront sensor and having a telescope and an auto-alignment system. The auto-alignment system is configured to adjust at least one first optical device in order to alter a line-of-sight of the wavefront sensor. The wavefront errors estimated by the wavefront sensor include a wavefront error resulting from the adjustment of the at least one first optical device. The beam director could further include at least one second optical device configured to correct for the wavefront errors. The at least one second optical device could include at least one deformable mirror.

A bottle storage includes: a housing portion that houses a bottle therein; a cooling-warming portion (cooling-warming devices) that is provided around the housing portion; a rotation portion (rotation motor) that rotates the bottle housed in the housing portion; an image capture portion that captures an image of the bottle housed in the housing portion; a controller that controls the bottle rotation performed by the rotation portion and the image capture performed by the image capture portion, based on the bottle image captured by the image capture portion; an information manager (storage manager) that identifies at least a brand of the bottle, based on the bottle image captured by the control of the controller; and an output portion (display portion) that outputs at least one of the brand identified by the information manager and relevant information regarding the identified brand.

An image processing unit includes an image acquisition unit, a blood vessel index value acquisition unit, and a blood vessel parameter calculation unit. The image acquisition unit acquires an endoscopic image captured by an endoscope. The blood vessel index value acquisition unit acquires a first blood vessel index value in a first observation condition and a second blood vessel index value in a second observation condition, as blood vessel index values of blood vessels of an observation target, using the endoscopic image acquired by the image acquisition unit. The blood vessel parameter calculation unit calculates a blood vessel parameter, using the first blood vessel index value and the second blood vessel index value.

A key identification system is provided. The key identification system comprises an imaging system to capture an image of a master key, and a logic to analyze the captured image. The imaging system may be capture an image of a groove in the master key from an angle between perpendicular and parallel to the blade of said master key. The logic analyzes the captured image to compare characteristics of the groove with groove characteristics of known key blanks to determine the likelihood of a match between the master key and a known key blank. The key identification system may further compensate for displacement or orientation of the master key with respect to the imaging system when analyzing characteristics of the groove.

Disclosed herein are methods and systems for analyzing a cross-sectional feature of a structural element on a semiconductor wafer to determine whether an isolated or a systemic failure to reach preselected parameters occurred.

A method of processing an image is disclosed. The method comprises decomposing the image into a plurality of channels, each being characterized by a different depth-of-field, and accessing a computer readable medium storing an in-focus dictionary defined over a plurality of dictionary atoms, and an out-of-focus dictionary defined over a plurality of sets of dictionary atoms, each set corresponding to a different out-of-focus condition. The method also comprises computing one or more sparse representations of the decomposed image over the dictionaries.

In general, a control method of an embodiment is to control a setting screen which enables selection between a first mode for forming an image using a color material for non-removal and a second mode for forming an image using a color material for removal. When the first mode is set, the control method enables, on a setting screen, the setting of a punching post-processing for making a hole in an image receiving medium and a non-punching post-processing for making no hole in the image receiving medium, and when the second mode is set, on the setting screen, disables the setting of the punching post-processing, but enables the setting of the non-punching post-processing.

In general, a control method of an embodiment is to control a setting screen which enables selection between a first mode for forming an image using a color material for non-removal and a second mode for forming an image using a color material for removal. When the first mode is set, the control method enables, on a setting screen, the setting of a punching post-processing for making a hole in an image receiving medium and a non-punching post-processing for making no hole in the image receiving medium, and when the second mode is set, on the setting screen, disables the setting of the punching post-processing, but enables the setting of the non-punching post-processing.

A method of processing an image is disclosed. The method comprises decomposing the image into a plurality of channels, each being characterized by a different depth-of-field, and accessing a computer readable medium storing an in-focus dictionary defined over a plurality of dictionary atoms, and an out-of-focus dictionary defined over a plurality of sets of dictionary atoms, each set corresponding to a different out-of-focus condition. The method also comprises computing one or more sparse representations of the decomposed image over the dictionaries.