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Found 8 papers, 2 papers with code
DualRefine: Self-Supervised Depth and Pose Estimation Through Iterative Epipolar Sampling and Refinement Toward Equilibrium
Our novel update pipeline uses a deep equilibrium model framework to iteratively refine depth estimates and a hidden state of feature maps by computing local matching costs based on epipolar geometry.
Lightweight HDR Camera ISP for Robust Perception in Dynamic Illumination Conditions via Fourier Adversarial Networks
The limited dynamic range of commercial compact camera sensors results in an inaccurate representation of scenes with varying illumination conditions, adversely affecting image quality and subsequently limiting the performance of underlying image processing algorithms.
DGSS : Domain Generalized Semantic Segmentation using Iterative Style Mining and Latent Representation Alignment
This framework allows us to achieve a domain generalized semantic segmentation algorithm with consistent performance without prior information of the target domain while relying on a single source.
GIQE: Generic Image Quality Enhancement via Nth Order Iterative Degradation
Visual degradations caused by motion blur, raindrop, rain, snow, illumination, and fog deteriorate image quality and, subsequently, the performance of perception algorithms deployed in outdoor conditions.
Correlate-and-Excite: Real-Time Stereo Matching via Guided Cost Volume Excitation
Volumetric deep learning approach towards stereo matching aggregates a cost volume computed from input left and right images using 3D convolutions.
Evaluating COPY-BLEND Augmentation for Low Level Vision Tasks
However, as these techniques destroy spatial relationship with neighboring regions, performance can be deteriorated when using them to train algorithms designed for low level vision tasks (low light image enhancement, image dehazing, deblurring, etc.)
Towards Domain Invariant Single Image Dehazing
However owing to fixed receptive field of convolutional kernels and non uniform haze distribution, assuring consistency between regions is difficult.
Retaining Image Feature Matching Performance Under Low Light Conditions
Poor image quality in low light images may result in a reduced number of feature matching between images.
