# US 10,311,554 B2 · Method of providing a sharpness measure for an image > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,311,554 B2 - Document ID: US10311554B2 - Status: Granted - Kind: B2 - Application number: 15/872,873 - Filed: 2018-01-16 - Published: 2018-09-06 - Granted: 2019-06-04 - Earliest claimed: 2017-03-01 - Assignees: FotoNation Limited - Applicants: FotoNation Limited - Inventors: Florin Nanu; Adrian Bobei; Alexandru Malaescu; Cosmin Clapon - Web page: https://patentgrep.com/patent/US10311554B2 ## CPC - G06T 5/73: Deblurring; Sharpening - G06T 7/0002: {Inspection of images, e.g. flaw detection} - G06T 7/13: Edge detection - G06T 7/42: using transform domain methods - G06V 10/34: Smoothing or thinning of the pattern; Morphological operations; Skeletonisation - G06V 40/161: {Detection; Localisation; Normalisation} - H04N 23/611: where the recognised objects include parts of the human body - H04N 23/67: Focus control based on electronic image sensor signals - H04N 23/673: {based on contrast or high frequency components of image signals, e.g. hill climbing method} - G06T 2207/30168: Image quality inspection - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION ## Abstract A method of providing a sharpness measure for an image comprises detecting an object region within an image; obtaining meta-data for the image; and scaling the chosen object region to a fixed size. A gradient map is calculated for the scaled object region and compared against a threshold determined for the image to provide a filtered gradient map of values exceeding the threshold. The threshold for the image is a function of at least: a contrast level for the detected object region, a distance to the subject and an ISO/gain used for image acquisition. A sharpness measure for the object region is determined as a function of the filtered gradient map values, the sharpness measure being proportional to the filtered gradient map values. ## Claims ### Claim 1 An image processing device arranged to, and comprising the steps of: detect an object region within an acquired image; obtain meta-data for the acquired image; scale the chosen object region to a fixed size; calculate a gradient map for the scaled object region; compare the gradient map against a threshold, wherein the threshold for the acquired image is a function of at least: a contrast level for the detected object region, a distance to the subject and an ISO value used for image acquisition; determine a sharpness measure for the object region as a function of the filtered gradient map values, the sharpness measure being proportional to the filtered gradient map values; and responsive to the sharpness measure indicating the object region is not sufficiently focussed, change focusing for acquirinq a subsequent image. ### Claim 2 (depends on 1) An image processing device according to claim 1 wherein the fixed size is 200×100 pixels. ### Claim 3 (depends on 1) An image processing device according to claim 1 wherein the object is a face. ### Claim 4 (depends on 3) An image processing device according to claim 3 wherein the method further arranged to crop the face region to only use a face region containing at least one eye as the chosen object region. ### Claim 5 (depends on 1) An image processing device according to claim 1 further arranged to calculate a noise measure for the object region by: splitting the filtered gradient map into an array; determining a mean sharpness for each cell of the array; sorting the mean sharpness values in order of magnitude; selecting a sub-range of sharpness values from the sorted sharpness values; and calculating said noise measure for the object region as a function of said sub-range of sharpness values. ### Claim 6 (depends on 5) An image processing device according to claim 5 wherein said sub-range comprises a group of the lowest valued sharpness values. ### Claim 7 (depends on 5) An image processing device according to claim 5 wherein the sharpness measure is a function of the noise measure for the object region, the sharpness measure being inversely proportional to the noise measure. ### Claim 8 (depends on 1) An image processing device according to claim 1 further arranged to calculate a threshold measure for the object region by: splitting the filtered gradient map into an array; determining a mean sharpness for each cell of the array; sorting the mean sharpness values in order of magnitude; the threshold for the object region, thr_Face, being defined as follows: thr_Face =mean(sorted_vals(high))*wb+mean(sorted_vals(low))*wr; where: sorted_vals(low) and sorted_vals(high) are groups of lowest and highest mean sharpness values respectively; wb αf(distance to subject) −(ISO value / contrast level * d); wr=1−wb; and d is a constant. ### Claim 9 (depends on 8) An image processing device according to claim 8 wherein the sharpness measure is proportional to the threshold measure for the object region. ### Claim 10 (depends on 8) An image processing device according to claim 8 wherein the threshold for the acquired image is proportional to sum of thresholds for ISO, contrast and distance adjusted by said threshold for the object region, thr_Face. ### Claim 11 (depends on 1) An image processing device according to claim 1 wherein the threshold tor the acquired Image is the maximum of: a constant; and a sum of thresholds for ISO, contrast and distance. ### Claim 12 (depends on 1) An image processing device according to claim 1 wherein the threshold for the acquired image is proportional to average luminance, ISO and distance. ### Claim 13 (depends on 1) An image processing device according to claim 1 further arranged to extract meta-data from the acquired image to determine parameters for each of said ISO, contrast and distance thresholds.