Multiple Light Source Dataset for Colour Research

使用双向长短期记忆网络生成颜色

Generation Of Colors using Bidirectional Long Short Term Memory Networks

本研究利用结合主动学习的双向长短期记忆网络，在专有数据集上开发新模型，用于对未命名颜色进行分类和命名，拓展了自然语言处理在颜色感知领域的应用。

BarkNet-Lite：一种轻量级纹理与颜色网络及用于孟加拉国基于树皮树种识别的BarkBD基准

BarkNet-Lite: A Lightweight Texture and Colour Network with the BarkBD Benchmark for Bark-Based Tree Species Recognition in Bangladesh

针对孟加拉国树种识别，提出轻量级BarkNet-Lite网络（2.96M参数）和BarkBD数据集，通过纹理与颜色双路径实现高精度，并验证了决策可解释性。

Cascading Convolutional Temporal Colour Constancy

Computational Colour Constancy (CCC) consists of estimating the colour of one or more illuminants in a scene and using them to remove unwanted chromatic distortions. Much research has focused on illuminant estimation for CCC on single images, with few attempts of leveraging the temporal information intrinsic in sequences of correlated images (e.g., the frames in a video), a task known as Temporal Colour Constancy (TCC). The state-of-the-art for TCC is TCCNet, a deep-learning architecture that uses a ConvLSTM for aggregating the encodings produced by CNN submodules for each image in a sequence.…

Visible Light-Based Human Visual System Conceptual Model

There exists a widely accepted set of assertions in the digital image and video coding literature, which are as follows: the Human Visual System (HVS) is more sensitive to luminance (often confused with brightness) than photon energies (often confused with chromaticity and chrominance). Passages similar to the following occur with high frequency in the peer reviewed literature and academic text books: "the HVS is much more sensitive to brightness than colour" and/or "the HVS is much more sensitive to luma than chroma". In this discussion paper, a Visible Light-Based Human Visual System (VL-HVS…

DichroGAN：从卫星图像中恢复海底空中颜色

DichroGAN: Towards Restoration of in-air Colours of Seafloor from Satellite Imagery

DichroGAN通过条件生成对抗网络从卫星图像中恢复海底的空中颜色，利用两步训练过程和水下图像形成方程实现高效恢复。

A Pipeline for Lenslet Light Field Quality Enhancement

In recent years, light fields have become a major research topic and their applications span across the entire spectrum of classical image processing. Among the different methods used to capture a light field are the lenslet cameras, such as those developed by Lytro. While these cameras give a lot of freedom to the user, they also create light field views that suffer from a number of artefacts. As a result, it is common to ignore a significant subset of these views when doing high-level light field processing. We propose a pipeline to process light field views, first with an enhanced processin…