Abstract: Systems and methods for performing image enhancement using neural networks implemented by channel-constrained hardware accelerators in accordance with embodiments of the invention are described.

Abstract: Described herein are systems and techniques for generating training data for use in training a machine learning model for image enhancement. The system may access a target image of a displayed video frame, wherein the target image represents a target output of the machine learning model. The system may access an input image of the displayed video frame, wherein the input image corresponds to the target image and represents an input to the machine learning model. The system may train the machine learning model using the target image and the input image corresponding to the target image to obtain a trained machine learning model.

Abstract: The techniques described herein provide for transforming images and/or quantizing images using nonlinear techniques. The transformed images can be used for image enhancement (e.g., transformation and/or quantization may be a pre-processing step prior to performing image enhancement). For example, the nonlinear intensity transformation techniques can provide for efficient denoising, better low-precision image processing, and/or the like, compared to performing image processing on the original image.

Abstract: Described herein are systems and techniques for generating training data for use in training a machine learning model for image enhancement. The system may access a target image of a displayed video frame, wherein the target image represents a target output of the machine learning model. The system may access an input image of the displayed video frame, wherein the input image corresponds to the target image and represents an input to the machine learning model. The system may train the machine learning model using the target image and the input image corresponding to the target image to obtain a trained machine learning model.

Abstract: An image captured by an imaging device in low light conditions may cause the captured image to have poor contrast, blurring, and otherwise not display one or more objects in the image clearly. According to various aspects, systems and methods are provided for enhancing images that are captured in low light conditions.

Abstract: Described herein are systems and techniques for generating training data for use in training a machine learning model for image enhancement. The system may access a target image of a displayed video frame, wherein the target image represents a target output of the machine learning model. The system may access an input image of the displayed video frame, wherein the input image corresponds to the target image and represents an input to the machine learning model. The system may train the machine learning model using the target image and the input image corresponding to the target image to obtain a trained machine learning model.