Intrinsic Image Decomposition
17 papers with code • 0 benchmarks • 5 datasets
Intrinsic Image Decomposition is the process of separating an image into its formation components such as reflectance (albedo) and shading (illumination). Reflectance is the color of the object, invariant to camera viewpoint and illumination conditions, whereas shading, dependent on camera viewpoint and object geometry, consists of different illumination effects, such as shadows, shading and inter-reflections. Using intrinsic images, instead of the original images, can be beneficial for many computer vision algorithms. For instance, for shape-from-shading algorithms, the shading images contain important visual cues to recover geometry, while for segmentation and detection algorithms, reflectance images can be beneficial as they are independent of confounding illumination effects. Furthermore, intrinsic images are used in a wide range of computational photography applications, such as material recoloring, relighting, retexturing and stylization.
Source: CNN based Learning using Reflection and Retinex Models for Intrinsic Image Decomposition
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Most implemented papers
Unsupervised Learning for Intrinsic Image Decomposition from a Single Image
Intrinsic image decomposition, which is an essential task in computer vision, aims to infer the reflectance and shading of the scene.
Unified Depth Prediction and Intrinsic Image Decomposition from a Single Image via Joint Convolutional Neural Fields
We present a method for jointly predicting a depth map and intrinsic images from single-image input.
Learning Intrinsic Image Decomposition from Watching the World
However, it is difficult to collect ground truth training data at scale for intrinsic images.
Joint Learning of Intrinsic Images and Semantic Segmentation
To that end, we propose a supervised end-to-end CNN architecture to jointly learn intrinsic image decomposition and semantic segmentation.
Learning Blind Video Temporal Consistency
Our method takes the original unprocessed and per-frame processed videos as inputs to produce a temporally consistent video.
Intrinsic Decomposition of Document Images In-the-Wild
However, document shadow or shading removal results still suffer because: (a) prior methods rely on uniformity of local color statistics, which limit their application on real-scenarios with complex document shapes and textures and; (b) synthetic or hybrid datasets with non-realistic, simulated lighting conditions are used to train the models.
Outdoor inverse rendering from a single image using multiview self-supervision
In this paper we show how to perform scene-level inverse rendering to recover shape, reflectance and lighting from a single, uncontrolled image using a fully convolutional neural network.
Physically Inspired Dense Fusion Networks for Relighting
While our proposed method applies to both one-to-one and any-to-any relighting problems, for each case we introduce problem-specific components that enrich the model performance: 1) For one-to-one relighting we incorporate normal vectors of the surfaces in the scene to adjust gloss and shadows accordingly in the image.
Intrinsic Image Transfer for Illumination Manipulation
We illustrate that all losses can be reduced without the necessity of taking an intrinsic image decomposition under the well-known spatial-varying illumination illumination-invariant reflectance prior knowledge.
Complementary Feature Enhanced Network with Vision Transformer for Image Dehazing
In this paper, firstly, we propose a new complementary feature enhanced framework, in which the complementary features are learned by several complementary subtasks and then together serve to boost the performance of the primary task.