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Found 22 papers, 0 papers with code
Automatic Exposure Compensation for Multi-Exposure Image Fusion
In conventional works, it has been pointed out that the quality of those multi-exposure images can be improved by adjusting the luminance of them.
Multi-Exposure Image Fusion Based on Exposure Compensation
This paper proposes a novel multi-exposure image fusion method based on exposure compensation.
A Pseudo Multi-Exposure Fusion Method Using Single Image
The proposed method enables us to produce pseudo multi-exposure images from a single image.
A Retinex-based Image Enhancement Scheme with Noise Aware Shadow-up Function
This paper proposes a novel image contrast enhancement method based on both a noise aware shadow-up function and Retinex (retina and cortex) decomposition.
Image Enhancement Network Trained by Using HDR images
Most of conventional image enhancement methods, including Retinex based methods, do not take into account restoring lost pixel values caused by clipping and quantizing.
A Noise-aware Enhancement Method for Underexposed Images
These methods, however, have two problems: (1) The loss of details in bright regions due to over-enhancement of contrast.
Multimedia
Convolutional Neural Networks Considering Local and Global features for Image Enhancement
To handle both local and global features, the proposed architecture consists of three networks: a local encoder, a global encoder, and a decoder.
Single-Shot High Dynamic Range Imaging with Spatially Varying Exposures Considering Hue Distortion
We proposes a novel single-shot high dynamic range imaging scheme with spatially varying exposures (SVE) considering hue distortion.
An Image Fusion Scheme for Single-Shot High Dynamic Range Imaging with Spatially Varying Exposures
This paper proposes a novel multi-exposure image fusion (MEF) scheme for single-shot high dynamic range imaging with spatially varying exposures (SVE).
Privacy-Preserving Machine Learning Using EtC Images
In this paper, we propose a novel privacy-preserving machine learning scheme with encrypted images, called EtC (Encryption-then-Compression) images.
Fixed smooth convolutional layer for avoiding checkerboard artifacts in CNNs
In an image-classification experiment with four CNNs: a simple CNN, VGG8, ResNet-18, and ResNet-101, applying the fixed layers to these CNNs is shown to improve the classification performance of all CNNs.
Image Transformation Network for Privacy-Preserving Deep Neural Networks and Its Security Evaluation
We propose a transformation network for generating visually-protected images for privacy-preserving DNNs.
Checkerboard-Artifact-Free Image-Enhancement Network Considering Local and Global Features
In this paper, we propose a novel convolutional neural network (CNN) that never causes checkerboard artifacts, for image enhancement.
CycleGAN without checkerboard artifacts for counter-forensics of fake-image detection
In this paper, we propose a novel CycleGAN without checkerboard artifacts for counter-forensics of fake-image detection.
Multi-color balance for color constancy
In this paper, we propose a novel multi-color balance adjustment for color constancy.
Separated-Spectral-Distribution Estimation Based on Bayesian Inference with Single RGB Camera
In this paper, we propose a novel method for separately estimating spectral distributions from images captured by a typical RGB camera.
Spatially varying white balancing for mixed and non-uniform illuminants
In this paper, we propose a novel white balance adjustment, called "spatially varying white balancing," for single, mixed, and non-uniform illuminants.
Self-Supervised Intrinsic Image Decomposition Network Considering Reflectance Consistency
Intrinsic image decomposition aims to decompose an image into illumination-invariant and illumination-variant components, referred to as ``reflectance'' and ``shading,'' respectively.
An Overview of Compressible and Learnable Image Transformation with Secret Key and Its Applications
In this paper, we focus on a class of image transformation referred to as learnable image encryption, which is applicable to privacy-preserving machine learning and adversarially robust defense.
Privacy-Preserving Image Classification Using Vision Transformer
In this paper, we propose a privacy-preserving image classification method that is based on the combined use of encrypted images and the vision transformer (ViT).
Image and Model Transformation with Secret Key for Vision Transformer
In this paper, we propose a combined use of transformed images and vision transformer (ViT) models transformed with a secret key.
Template matching with white balance adjustment under multiple illuminants
In this paper, we propose a novel template matching method with a white balancing adjustment, called N-white balancing, which was proposed for multi-illuminant scenes.
