Abstract: This present invention discloses a personalized and user specific system for editing an image. The system utilizes voice commands that are supplemented by semantic learning to form semantic descriptors. In addition, the system also utilizes user preferences to term an edited image. The system is utilized especially by novice photo editors, to provide a desired filter effect to the image with relative ease.

Abstract: A multi-frame image fusion method and system, an electronic device, and a storage medium are provided including performing image alignment based on a frame with highest exposure sharpness among a plurality of frames of images, wherein the image alignment includes extracting feature points of each of the plurality of frames to make the plurality of frames have a common spatial layout; calculating a fusion region of each frame based on local light source diffusion weight information in a spatial domain; performing image fusion on the plurality of frames with different exposures based on the fusion region, wherein the image fusion includes fusing the plurality of frames with different exposures into a fused image; and compressing the fused image to a preset dynamic range section. During the image fusion process, the local information is introduced into images to make the fused image possess a higher dynamic range and a lower halo.

Abstract: The present invention discloses system and method for enhancing an image based on non-local features. The invention includes an end-to-end trainable and guided method including feature extraction block, non-local feature generator and non-local feature enhancement block, to deal with low-level image problem by using the non-local feature concept. The invention also deploys a non-local feature merge block to rectify translated features and improve non-local feature further to finally reconstruct the rectified features to form an enhanced image.

Abstract: A method performed by an electronic device with one or more processors and memory includes receiving a plurality of frames of an image; selecting one frame of the plurality of frames as a main frame thereby leaving the rest of the plurality of frames as reference frames; aligning the reference frames with the main frame; determining fusion weights for a respective reference frame of the reference frames by comparing the main frame and the respective reference frame; and obtaining a weighted combination of the main frame and the reference frames based on the fusion weights. An electronic device configured for performing the method and a computer readable storage medium storing instructions for performing the method are also disclosed.

Abstract: The present invention discloses an intelligent cloud-based photo enhancement system to improve the quality and aesthetics of an image captured via an electronic device. The cloud-based photo enhancement system comprises a cloud database containing a collection of high-quality photos taken at various locations worldwide and under different environment settings. Information from photos stored in the cloud database taken using better photographic capability cameras is used to improve details that the new photo fails to capture. The system relies not only on machine learning models but also utilizes data associated with the new image collected from the hardware of the electronic device such as camera settings, GPS and phone sensors, which results in more reliable information.

Abstract: The present invention discloses system and method for enhancing an image based on non-local features. The invention includes an end-to-end trainable and guided method including feature extraction block, non-local feature generator and non-local feature enhancement block, to deal with low-level image problem by using the non-local feature concept. The invention also deploys a non-local feature merge block to rectify translated features and improve non-local feature further to finally reconstruct the rectified features to form an enhanced image.

Abstract: A method of age and gender estimation, comprising receiving an input image, detecting a facial image within the input image, estimating a head pose based on a set of facial image intensities of the facial image, Wherein the head pose is expressed as a yaw, a pitch and a roll, determining whether the yaw, the pitch and the roll of the head pose is less than a predetermined threshold, aligning the facial image if the yaw, the pitch and the roll of the head pose are less than the predetermined threshold and predicting an age and a gender of the aligned facial image.

Abstract: The present invention provides an intelligent and a flexible video enhancement system with improved visual quality of the video for better user experience. The video enhancement system enhances various types of videos and provides a user with better visual experience based on video corresponding features and personal preferences. The video enhancement system includes a dynamic analysis and adaptive processing techniques for enhancing video quality for seamless user experience.

Abstract: The present invention discloses a multi-frame image super resolution system that utilizes both deep learning models and traditional models of enhancing the resolution of an image so that minimal computational resources are used. A frame alignment module of the invention aligns the frames of the image after which a processing module configured within the system process the Y and the UV channels of the image by using multiple deep and traditional resolution enhancement models. A merging unit merges the output of the processors to produce a super resolution image incorporating the advantages of both of the image enhancement methods.

Abstract: An example photographic feedback method, comprising, inputting a query image, a camera setting, a location, a temporal indicia and an environmental condition, detecting an object within the query image, projecting the location of the object, classifying the environmental condition at the location of the object, querying features of the query image of the object at the environmental condition of the projected location of the object, determining a closest image to the query image for the object at the environmental condition of the projected location of the object having a highly rated value via a cloud database and sending an updated location and at least one updated camera setting for an updated image.

Abstract: An example photographic feedback method, comprising, inputting a query image, a camera setting, a location, a temporal indicia and an environmental condition, detecting an object within the query image, projecting the location of the object, classifying the environmental condition at the location of the object, querying features of the query image of the object at the environmental condition of the projected location of the object, determining a closest image to the query image for the object at the environmental condition of the projected location of the object having a highly rated value via a cloud database and sending an updated location and at least one updated camera setting for an updated image.

Abstract: A method for demosaicing a raw image includes: (1) horizontally-interpolating a green channel formed of primary pixel-values Bg(x,y)g to yield a horizontally-interpolated green channel that includes both Bg(x,y)g and non-primary pixel-values Igh(x,y)r,b; (2) modifying each Igh(x,y)r,b, by horizontally-neighboring pixel-values, to yield a refined horizontally-interpolated green channel; (3) vertically-interpolating the green channel to yield a vertically-interpolated green channel that includes pixel-values Igv(x,y)r,b; (4) modifying each Igv(x,y)r,b by vertically-neighboring pixel-values, to yield a refined vertically-interpolated green channel; (5) generating a full-resolution green channel from the refined interpolated green channels and gradients thereof; (6) generating a full-resolution red channel by determining red pixel-values from a local-red mean value of neighboring pixel-values and the full-resolution green channel; (7) generating a full-resolution blue channel by determining pixel-values from a lo

Abstract: A method for demosaicing a raw image includes: (1) horizontally-interpolating a green channel formed of primary pixel-values Bg(x,y)g to yield a horizontally-interpolated green channel that includes both Bg(x,y)g and non-primary pixel-values Igh(x,y)r,b; (2) modifying each Igh(x,y)r,b, by horizontally-neighboring pixel-values, to yield a refined horizontally-interpolated green channel; (3) vertically-interpolating the green channel to yield a vertically-interpolated green channel that includes pixel-values Igv(x,y)r,b; (4) modifying each Igv(x,y)r,b by vertically-neighboring pixel-values, to yield a refined vertically-interpolated green channel; (5) generating a full-resolution green channel from the refined interpolated green channels and gradients thereof; (6) generating a full-resolution red channel by determining red pixel-values from a local-red mean value of neighboring pixel-values and the full-resolution green channel; (7) generating a full-resolution blue channel by determining pixel-values from a lo