# US 2026/0073591 A1 · Lightness models for image visual enhancement > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0073591 A1 - Document ID: US20260073591A1 - Status: Published application - Kind: A1 - Application number: 19/173,247 - Filed: 2025-04-08 - Published: 2026-03-12 - Earliest claimed: 2024-09-06 - Assignees: Samsung Electronics Co., Ltd. - Applicants: Samsung Electronics Co., Ltd. - Inventors: Yingen Xiong - Web page: https://patentgrep.com/patent/US20260073591A1 ## CPC - G06T 11/60: Creating or editing images; Combining images with text - G06T 5/60: using machine learning, e.g. neural networks - G06T 5/70: Denoising; Smoothing - G06T 5/92: based on global image properties - G06T 7/0002: {Inspection of images, e.g. flaw detection} - G06T 7/70: Determining position or orientation of objects or cameras (camera calibration G06T7/80) - G06V 10/60: relating to illumination properties, e.g. using a reflectance or lighting model - H04N 9/73: Colour balance circuits, e.g. white balance circuits or colour temperature control (camera processing pipelines for colour balance H04N23/88) - H04N 23/71: Circuitry for evaluating the brightness variation - G06T 2207/10024: Color image - G06T 2207/20081: Training; Learning - G06T 2207/30168: Image quality inspection - H04N 23/88: for colour balance, e.g. white-balance circuits or colour temperature control - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION ## Abstract A method includes obtaining an image frame using at least one imaging sensor. The method also includes selecting one of a plurality of lightness models based on visual quality of the image frame, applying the selected lightness model to the image frame in order to generate a modified image frame, and rendering an image for display based on the modified image frame. The visual quality is associated with a lightness condition of the image frame. Different lightness models are associated with different lightness conditions. ## Claims ### Claim 1 An apparatus comprising: at least one imaging sensor configured to capture an image frame; and at least one processing device configured to: select one of a plurality of lightness models based on visual quality of the image frame, the visual quality associated with a lightness condition of the image frame, different lightness models associated with different lightness conditions; apply the selected lightness model to the image frame in order to generate a modified image frame; and render an image for display based on the modified image frame. ### Claim 2 (depends on 1) The apparatus of claim 1, wherein: the at least one processing device is further configured to generate the lightness models; and to generate the lightness models, the at least one processing device is configured to: determine one or more thresholds to define the different lightness conditions; and for each of the defined lightness conditions: capture multiple image frames at the defined lightness condition; create a dataset for the defined lightness condition based on the multiple image frames captured at the defined lightness condition; and generate the lightness model for the defined lightness condition with one or more parameters based on the dataset. ### Claim 3 (depends on 1) The apparatus of claim 1, wherein, to select one of the plurality of lightness models, the at least one processing device is configured to: measure a signal-to-noise ratio (SNR) and lightness level of the image frame; determine whether the measured lightness level falls outside a lightness level threshold; determine whether the measured SNR is greater than an SNR threshold; and in response to the measured SNR being less than the SNR threshold, select the lightness model having parameters matching the measured SNR and measured lightness level of the image frame. ### Claim 4 (depends on 3) The apparatus of claim 3, wherein the at least one processing device is further configured to: in response to a determination that the measured SNR is greater than the SNR threshold, select at least one of a white balance algorithm, a histogram equalization algorithm, an image re-lighting algorithm, or a lightness adjustment algorithm for application to the image frame. ### Claim 5 (depends on 1) The apparatus of claim 1, wherein: the at least one processing device is further configured to apply a transformation to the modified image frame in order to generate a transformed image frame; and to render the image for display, the at least one processing device is configured to render the transformed image frame. ### Claim 6 (depends on 1) The apparatus of claim 1, wherein the at least one processing device is further configured to: generate a dataset using the image frame; and update a specified one of the lightness models using the dataset; and wherein the at least one processing device is configured to apply the updated specified lightness model to the image frame in order to generate the modified image frame. ### Claim 7 (depends on 1) The apparatus of claim 1, wherein: the image frame comprises a first image frame; the at least one imaging sensor is configured to capture a second image frame sequentially with the first image frame; and the at least one processing device is further configured to: obtain a difference between user head poses associated with the first and second image frames; determine whether the difference is greater than a head pose change threshold; in response to a determination that the difference is not greater than the head pose change threshold, determine whether visual quality of the second image frame falls outside one or more thresholds utilizing a signal-to-noise ratio (SNR) and lightness level of the first image frame; and in response to a determination that the difference is greater than the head pose change threshold, determine whether the visual quality of the second image frame falls outside the one or more thresholds utilizing an SNR and lightness level of the second image frame. ### Claim 8 A method comprising: obtaining an image frame; selecting one of a plurality of lightness models based on visual quality of the image frame, the visual quality associated with a lightness condition of the image frame, different lightness models associated with different lightness conditions; applying the selected lightness model to the image frame in order to generate a modified image frame; and rendering an image for display based on the modified image frame. ### Claim 9 (depends on 8) The method of claim 8, further comprising: generating the lightness models by: determining one or more thresholds to define the different lightness conditions; and for each of the defined lightness conditions: capturing multiple image frames at the defined lightness condition; creating a dataset for the defined lightness condition based on the multiple image frames captured at the defined lightness condition; and generating the lightness model for the defined lightness condition with one or more parameters based on the dataset. ### Claim 10 (depends on 8) The method of claim 8, wherein selecting one of the plurality of lightness models comprises: measuring a signal-to-noise ratio (SNR) and lightness level of the image frame; determining whether the measured lightness level falls outside a lightness level threshold; determining whether the measured SNR is greater than an SNR threshold; and in response to the measured SNR being less than the SNR threshold, selecting the lightness model having parameters matching the measured SNR and measured lightness level of the image frame. ### Claim 11 (depends on 10) The method of claim 10, further comprising: in response to a determination that the measured SNR is greater than the SNR threshold, selecting at least one of a white balance algorithm, a histogram equalization algorithm, an image re-lighting algorithm, or a lightness adjustment algorithm for application to the image frame. ### Claim 12 (depends on 8) The method of claim 8, further comprising: applying a transformation to the modified image frame in order to generate a transformed image frame; wherein rendering the image for display comprises rendering the transformed image frame. ### Claim 13 (depends on 8) The method of claim 8, further comprising: generating a dataset using the image frame; and updating a specified one of the lightness models using the dataset; wherein the updated specified lightness model is applied to the image frame in order to generate the modified image frame. ### Claim 14 (depends on 8) The method of claim 8, wherein: the image frame comprises a first image frame; and the method further comprises: capturing a second image frame sequentially with the first image frame; obtaining a difference between user head poses associated with the first and second image frames; determining whether the difference is greater than a head pose change threshold; in response to a determination that the difference is not greater than the head pose change threshold, determining whether visual quality of the second image frame falls outside one or more thresholds utilizing a signal-to-noise ratio (SNR) and lightness level of the first image frame; and in response to a determination that the difference is greater than the head pose change threshold, determining whether the visual quality of the second image frame falls outside the one or more thresholds utilizing an SNR and lightness level of the second image frame. ### Claim 15 A non-transitory machine readable medium containing instructions that when executed cause at least one processor of an electronic device to: obtain an image frame; select one of a plurality of lightness models based on visual quality of the image frame, the visual quality associated with a lightness condition of the image frame, different lightness models associated with different lightness conditions; apply the selected lightness model to the image frame in order to generate a modified image frame; and render an image for display based on the modified image frame. ### Claim 16 (depends on 15) The non-transitory machine readable medium of claim 15, further containing instructions that when executed cause the at least one processor to generate the lightness models; wherein the instructions that when executed cause the at least one processor to generate the lightness models comprise instructions that when executed cause the at least one processor to: determine one or more thresholds to define the different lightness conditions; and for each of the defined lightness conditions: capture multiple image frames at the defined lightness condition; create a dataset for the defined lightness condition based on the multiple image frames captured at the defined lightness condition; and generate the lightness model for the defined lightness condition with one or more parameters based on the dataset. ### Claim 17 (depends on 15) The non-transitory machine readable medium of claim 15, wherein the instructions that when executed cause the at least one processor to select one of the plurality of lightness models comprise instructions that when executed cause the at least one processor to: measure a signal-to-noise ratio (SNR) and lightness level of the image frame; determine whether the measured lightness level falls outside a lightness level threshold; determine whether the measured SNR is greater than an SNR threshold; and in response to the measured SNR being less than the SNR threshold, select the lightness model having parameters matching the measured SNR and measured lightness level of the image frame. ### Claim 18 (depends on 17) The non-transitory machine readable medium of claim 17, further containing instructions that when executed cause the at least one processor, in response to a determination that the measured SNR is greater than the SNR threshold, to select at least one of a white balance algorithm, a histogram equalization algorithm, an image re-lighting algorithm, or a lightness adjustment algorithm for application to the image frame. ### Claim 19 (depends on 15) The non-transitory machine readable medium of claim 15, further containing instructions that when executed cause the at least one processor to apply a transformation to the modified image frame in order to generate a transformed image frame. ### Claim 20 (depends on 15) The non-transitory machine readable medium of claim 15, further containing instructions that when executed cause the at least one processor to: generate a dataset using the image frame; and update a specified one of the lightness models using the dataset.