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Found 12 papers, 4 papers with code
Little Strokes Fell Great Oaks: Boosting the Hierarchical Features for Multi-exposure Image Fusion
In recent years, deep learning networks have made remarkable strides in the domain of multi-exposure image fusion.
A Generalized Physical-knowledge-guided Dynamic Model for Underwater Image Enhancement
To tackle these challenges, we design a Generalized Underwater image enhancement method via a Physical-knowledge-guided Dynamic Model (short for GUPDM), consisting of three parts: Atmosphere-based Dynamic Structure (ADS), Transmission-guided Dynamic Structure (TDS), and Prior-based Multi-scale Structure (PMS).
Towards General and Fast Video Derain via Knowledge Distillation
As a common natural weather condition, rain can obscure video frames and thus affect the performance of the visual system, so video derain receives a lot of attention.
Histogram-guided Video Colorization Structure with Spatial-Temporal Connection
Video colorization, aiming at obtaining colorful and plausible results from grayish frames, has aroused a lot of interest recently.
Transmission and Color-guided Network for Underwater Image Enhancement
To deal with the color deviation problem, we design a Dynamic Color-guided Module (DCM) to post-process the enhanced image color.
Realistic Bokeh Effect Rendering on Mobile GPUs, Mobile AI & AIM 2022 challenge: Report
In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based bokeh effect rendering approach that can run on modern smartphone GPUs using TensorFlow Lite.
Triple-level Model Inferred Collaborative Network Architecture for Video Deraining
In this paper, we develop a model-guided triple-level optimization framework to deduce network architecture with cooperating optimization and auto-searching mechanism, named Triple-level Model Inferred Cooperating Searching (TMICS), for dealing with various video rain circumstances.
A General Descent Aggregation Framework for Gradient-based Bi-level Optimization
In this work, we formulate BLOs from an optimistic bi-level viewpoint and establish a new gradient-based algorithmic framework, named Bi-level Descent Aggregation (BDA), to partially address the above issues.
Optimization-Inspired Learning with Architecture Augmentations and Control Mechanisms for Low-Level Vision
Specifically, by introducing a general energy minimization model and formulating its descent direction from different viewpoints (i. e., in a generative manner, based on the discriminative metric and with optimality-based correction), we construct three propagative modules to effectively solve the optimization models with flexible combinations.
A Generic First-Order Algorithmic Framework for Bi-Level Programming Beyond Lower-Level Singleton
In recent years, a variety of gradient-based first-order methods have been developed to solve bi-level optimization problems for learning applications.
Investigating Task-driven Latent Feasibility for Nonconvex Image Modeling
Properly modeling latent image distributions plays an important role in a variety of image-related vision problems.
Investigating Customization Strategies and Convergence Behaviors of Task-specific ADMM
Alternating Direction Method of Multiplier (ADMM) has been a popular algorithmic framework for separable optimization problems with linear constraints.
